Every time you type a message on your smartphone, your keyboard learns a little more about you. It notices your favourite words, your common misspellings, the names of people you text most often. For years, this intimate knowledge was hoovered up and shipped to distant servers, where tech giants analysed your linguistic fingerprints alongside billions of others. Then, around 2017, something changed. Google began training its Gboard keyboard using a technique called federated learning, promising that your typing data would never leave your device. The raw text of your most private messages, they assured users, would stay exactly where it belonged: on your phone.
It sounds like a privacy advocate's dream. But beneath this reassuring narrative lies a more complicated reality, one where mathematical guarantees collide with practical vulnerabilities, where corporate interests shape the definition of “privacy,” and where the gap between what users understand and what actually happens grows wider by the day. As AI systems increasingly rely on techniques like federated learning and differential privacy to protect sensitive information, a fundamental question emerges: are these technical solutions genuine shields against surveillance, or are they elaborate mechanisms that create new attack surfaces whilst giving companies plausible deniability?
The Machinery of Privacy Preservation
To understand whether federated learning and differential privacy actually work, you first need to understand what they are and how they operate. These are not simple concepts, and that complexity itself becomes part of the problem.
Federated learning, first formally introduced by Google researchers in 2016, fundamentally reimagines how machine learning models are trained. In the traditional approach, organisations collect vast quantities of data from users, centralise it on their servers, and train AI models on this aggregated dataset. Federated learning inverts this process. Instead of bringing data to the model, it brings the model to the data.
The process works through a carefully orchestrated dance between a central server and millions of edge devices, typically smartphones. The server distributes an initial model to participating devices. Each device trains that model using only its local data, perhaps the messages you have typed, the photos you have taken, or the websites you have visited. Crucially, the raw data never leaves your device. Instead, each device sends back only the model updates, the mathematical adjustments to weights and parameters that represent what the model learned from your data. The central server aggregates these updates from thousands or millions of devices, incorporates them into a new global model, and distributes this improved version back to the devices. The cycle repeats until the model converges.
The technical details matter here. Google's implementation in Gboard uses the FederatedAveraging algorithm, with between 100 and 500 client updates required to close each round of training. On average, each client processes approximately 400 example sentences during a single training epoch. The federated system converges after about 3000 training rounds, during which 600 million sentences are processed by 1.5 million client devices.
Differential privacy adds another layer of protection. Developed by computer scientists including Cynthia Dwork of Harvard University, who received the National Medal of Science in January 2025 for her pioneering contributions to the field, differential privacy provides a mathematically rigorous guarantee about information leakage. The core idea is deceptively simple: if you add carefully calibrated noise to data or computations, you can ensure that the output reveals almost nothing about any individual in the dataset.
The formal guarantee states that an algorithm is differentially private if its output looks nearly identical whether or not any single individual's data is included in the computation. This is measured by a parameter called epsilon, which quantifies the privacy loss. A smaller epsilon means stronger privacy but typically comes at the cost of utility, since more noise obscures more signal.
The noise injection typically follows one of several mechanisms. The Laplace mechanism adds noise calibrated to the sensitivity of the computation. The Gaussian mechanism uses a different probability distribution, factoring in both sensitivity and privacy parameters. Each approach has trade-offs in terms of accuracy, privacy strength, and computational efficiency.
When combined, federated learning and differential privacy create what appears to be a formidable privacy fortress. Your data stays on your device. The model updates sent to the server are aggregated with millions of others. Additional noise is injected to obscure individual contributions. In theory, even if someone intercepted everything being transmitted, they would learn nothing meaningful about you.
In practice, however, the picture is considerably more complicated.
When Privacy Promises Meet Attack Vectors
The security research community has spent years probing federated learning systems for weaknesses, and they have found plenty. One of the most troubling discoveries involves gradient inversion attacks, which demonstrate that model updates themselves can leak significant information about the underlying training data.
A gradient, in machine learning terms, is the mathematical direction and magnitude by which model parameters should be adjusted based on training data. Researchers have shown that by analysing these gradients, attackers can reconstruct substantial portions of the original training data. A 2025 systematic review published in Frontiers in Computer Science documented how gradient-guided diffusion models can now achieve “visually perfect recovery of images up to 512x512 pixels” from gradient information alone.
The evolution of these attacks has been rapid. Early gradient inversion techniques required significant computational resources and produced only approximate reconstructions. Modern approaches using fine-tuned generative models reduce mean squared error by an order of magnitude compared to classical methods, whilst simultaneously achieving inference speeds a million times faster and demonstrating robustness to gradient noise.
The implications are stark. Even though federated learning never transmits raw data, the gradients it does transmit can serve as a detailed map back to that data. A team of researchers demonstrated this vulnerability specifically in the context of Google's Gboard, publishing their findings in a paper pointedly titled “Two Models are Better than One: Federated Learning is Not Private for Google GBoard Next Word Prediction.” Their work showed that the word order and actual sentences typed by users could be reconstructed with high fidelity from the model updates alone.
Beyond gradient leakage, federated learning systems face threats from malicious participants. In Byzantine attacks, compromised devices send deliberately corrupted model updates designed to poison the global model. Research published by Fang et al. at NDSS in 2025 demonstrated that optimised model poisoning attacks can cause “1.5x to 60x higher reductions in the accuracy of FL models compared to previously discovered poisoning attacks.” This suggests that existing defences against malicious participants are far weaker than previously assumed.
Model inversion attacks present another concern. These techniques attempt to reverse-engineer sensitive information about training data by querying a trained model. A February 2025 paper on arXiv introduced “federated unlearning inversion attacks,” which exploit the model differences before and after data deletion to expose features and labels of supposedly forgotten data. As regulations like the GDPR establish a “right to be forgotten,” the very mechanisms designed to delete user data may create new vulnerabilities.
Differential privacy, for its part, is not immune to attack either. Research has shown that DP-SGD, the standard technique for adding differential privacy to deep learning, cannot prevent certain classes of model inversion attacks. A study by Zhang et al. demonstrated that their generative model inversion attack in face recognition settings could succeed even when the target model was trained with differential privacy guarantees.
The Census Bureau's Cautionary Tale
Perhaps the most instructive real-world example of differential privacy's limitations comes from the US Census Bureau's adoption of the technique for the 2020 census. This was differential privacy's biggest test, applied to data that would determine congressional representation and the allocation of hundreds of billions of dollars in federal funds.
The results were controversial. Research published in PMC in 2024 found that “the total population counts are generally preserved by the differential privacy algorithm. However, when we turn to population subgroups, this accuracy depreciates considerably.” The same study documented that the technique “introduces disproportionate discrepancies for rural and non-white populations,” with “significant changes in estimated mortality rates” occurring for less populous areas.
For demographers and social scientists, the trade-offs proved troubling. A Gates Open Research study quantified the impact: when run on historical census data with a privacy budget of 1.0, the differential privacy system produced errors “similar to that of a simple random sample of 50% of the US population.” In other words, protecting privacy came at the cost of effectively throwing away half the data. With a privacy budget of 4.0, the error rate decreased to approximate that of a 90 percent sample, but privacy guarantees correspondingly weakened.
The Census Bureau faced criticism from data users who argued that local governments could no longer distinguish between actual errors in their data and noise introduced by the privacy algorithm. The structural inaccuracy preserved state-level totals whilst “intentionally distorting characteristic data at each sub-level.”
This case illuminates a fundamental tension in differential privacy: the privacy-utility trade-off is not merely technical but political. Decisions about how much accuracy to sacrifice for privacy, and whose data bears the greatest distortion, are ultimately value judgements that mathematics alone cannot resolve.
Corporate Privacy, Corporate Interests
When technology companies tout their use of federated learning and differential privacy, it is worth asking what problems these techniques actually solve, and for whom.
Google's deployment of federated learning in Gboard offers a revealing case study. The company has trained and deployed more than twenty language models for Gboard using differential privacy, achieving what they describe as “meaningfully formal DP guarantees” with privacy parameters (rho-zCDP) ranging from 0.2 to 2. This sounds impressive, but the privacy parameters alone do not tell the full story.
Google applies the DP-Follow-the-Regularized-Leader algorithm specifically because it achieves formal differential privacy guarantees without requiring uniform sampling of client devices, a practical constraint in mobile deployments. The company reports that keyboard prediction accuracy improved by 24 percent through federated learning, demonstrating tangible benefits from the approach.
Yet Google still learns aggregate patterns from billions of users. The company still improves its products using that collective intelligence. Federated learning changes the mechanism of data collection but not necessarily the fundamental relationship between users and platforms. As one Google research publication frankly acknowledged, “improvements to this technology will benefit all users, although users are only willing to contribute if their privacy is ensured.”
The tension becomes even starker when examining Meta, whose platforms represent some of the largest potential deployments of privacy-preserving techniques. A 2025 analysis in Springer Nature noted that “approximately 98% of Meta's revenue derives from targeted advertising, a model that depends heavily on the collection and analysis of personal data.” This business model “creates a strong incentive to push users to sacrifice privacy, raising ethical concerns.”
Privacy-preserving techniques can serve corporate interests in ways that do not necessarily align with user protection. They enable companies to continue extracting value from user data whilst reducing legal and reputational risks. They provide technical compliance with regulations like the GDPR without fundamentally changing surveillance-based business models.
Apple presents an interesting contrast. The company has integrated differential privacy across its ecosystem since iOS 10 in 2016, using it for features ranging from identifying popular emojis to detecting domains that cause high memory usage in Safari. In iOS 17, Apple applied differential privacy to learn about popular photo locations without identifying individual users. With iOS 18.5, the company extended these techniques to train certain Apple Intelligence features, starting with Genmoji.
Apple's implementation deploys local differential privacy, meaning data is randomised before leaving the device, so Apple's servers never receive raw user information. Users can opt out entirely through Settings, and privacy reports are visible in device settings, providing a degree of transparency unusual in the industry.
Apple's approach differs from Google's in that the company does not derive the majority of its revenue from advertising. Yet even here, questions arise about transparency and user understanding. The technical documentation is dense, the privacy parameters are not prominently disclosed, and the average user has no practical way to verify the claimed protections.
The Understanding Gap
The gap between technical privacy guarantees and user comprehension represents perhaps the most significant challenge facing these technologies. Differential privacy's mathematical rigour means nothing if users cannot meaningfully consent to, or even understand, what they are agreeing to.
Research on the so-called “privacy paradox” consistently finds a disconnect between stated privacy concerns and actual behaviour. A study analysing Alipay users found “no relationship between respondents' self-stated privacy concerns and their number of data-sharing authorizations.” Rather than indicating irrational behaviour, the researchers argued this reflects the complexity of privacy decisions in context.
A 2024 Deloitte survey found that less than half of consumers, 47 percent, trust online services to protect their data. Yet a separate survey by HERE Technologies found that more than two-thirds of consumers expressed willingness to share location data, with 79 percent reporting they would allow navigation services to access their data. A study of more than 10,000 respondents across 10 countries found 53 percent expressing concern about digital data sharing, even as 70 percent indicated growing willingness to share location data when benefits were clear.
This is not necessarily a paradox so much as an acknowledgment that privacy decisions involve trade-offs that differ by context, by benefit received, and by trust in the collecting entity. But federated learning and differential privacy make these trade-offs harder to evaluate, not easier. When a system claims to be “differentially private with epsilon equals 4,” what does that actually mean for the user? When federated learning promises that “your data never leaves your device,” does that account for the information that gradients can leak?
The French data protection authority CNIL has recommended federated learning as a “data protection measure from the outset,” but also acknowledged the need for “explainability and traceability measures regarding the outputs of the system.” The challenge is that these systems are inherently difficult to explain. Their privacy guarantees are statistical, not absolute. They protect populations, not necessarily individuals. They reduce risk without eliminating it.
Healthcare: High Stakes, Conflicting Pressures
Nowhere are the tensions surrounding privacy-preserving AI more acute than in healthcare, where the potential benefits are enormous and the sensitivity of data is extreme.
NVIDIA's Clara federated learning platform exemplifies both the promise and the complexity. Clara enables hospitals to collaboratively train AI models without sharing patient data. Healthcare institutions including the American College of Radiology, Massachusetts General Hospital and Brigham and Women's Hospital's Center for Clinical Data Science, and UCLA Health have partnered with NVIDIA on federated learning initiatives.
In the United Kingdom, NVIDIA partnered with King's College London and the AI company Owkin to create a federated learning platform for the National Health Service, initially connecting four of London's premier teaching hospitals. The Owkin Connect platform uses blockchain technology to capture and trace all data used for model training, providing an audit trail that traditional centralised approaches cannot match.
During the COVID-19 pandemic, NVIDIA coordinated a federated learning study involving twenty hospitals globally to train models predicting clinical outcomes in symptomatic patients. The study demonstrated that federated models could outperform models trained on any single institution's data alone, suggesting that the technique enables collaboration that would otherwise be impossible due to privacy constraints.
In the pharmaceutical industry, the MELLODDY project brought together ten pharmaceutical companies in Europe to apply federated learning to drug discovery. The consortium pools the largest existing chemical compound library, more than ten million molecules and one billion assays, whilst ensuring that highly valuable proprietary data never leaves each company's control. The project runs on the open-source Substra framework and employs distributed ledger technology for full traceability.
These initiatives demonstrate genuine value. Healthcare AI trained on diverse populations across multiple institutions is likely to generalise better than AI trained on data from a single hospital serving a particular demographic. Federated learning makes such collaboration possible in contexts where data sharing would be legally prohibited or practically impossible.
But the same vulnerabilities that plague federated learning elsewhere apply here too, perhaps with higher stakes. Gradient inversion attacks could potentially reconstruct medical images. Model poisoning by a malicious hospital could corrupt a shared diagnostic tool. The privacy-utility trade-off means that stronger privacy guarantees may come at the cost of clinical accuracy.
Regulation Catches Up, Slowly
The regulatory landscape is evolving to address these concerns, though the pace of change struggles to keep up with technological development.
In the European Union, the AI Act took full effect on 2 August 2025, establishing transparency obligations for general-purpose AI systems. In November 2025, the European Commission published the Digital Omnibus proposal, streamlining the relationship between the Data Act, GDPR, and AI Act. The proposal includes clarification that organisations “may rely on legitimate interests to process personal data for AI-related purposes, provided they fully comply with all existing GDPR safeguards.”
In the United States, NIST finalised guidelines for evaluating differential privacy guarantees in March 2025, fulfilling an assignment from President Biden's Executive Order on Safe, Secure, and Trustworthy AI from October 2023. The guidelines provide a framework for assessing privacy claims but acknowledge the complexity of translating mathematical parameters into practical privacy assurances.
The market is responding to these regulatory pressures. The global privacy-enhancing technologies market reached 3.12 billion US dollars in 2024, projected to grow to 12.09 billion dollars by 2030. The federated learning platforms market, valued at 150 million dollars in 2023, is forecast to reach 2.3 billion dollars by 2032, reflecting a compound annual growth rate of 35.4 percent. The average cost of a data breach reached 4.88 million dollars in 2024, and industry analysts estimate that 75 percent of the world's population now lives under modern privacy regulations.
This growth suggests that corporations see privacy-preserving techniques as essential infrastructure for the AI age, driven as much by regulatory compliance and reputational concerns as by genuine commitment to user protection.
The Security Arms Race
The relationship between privacy-preserving techniques and the attacks against them resembles an arms race, with each advance prompting countermeasures that prompt new attacks in turn.
Defensive techniques have evolved significantly. Secure aggregation protocols encrypt model updates so that the central server only learns the aggregate, not individual contributions. Homomorphic encryption allows computation on encrypted data, theoretically enabling model training without ever decrypting sensitive information. Byzantine-robust aggregation algorithms attempt to detect and exclude malicious model updates.
Each defence has limitations. Secure aggregation protects against honest-but-curious servers but does not prevent sophisticated attacks like Scale-MIA, which researchers demonstrated can reconstruct training data even from securely aggregated updates. Homomorphic encryption imposes significant computational overhead and is not yet practical for large-scale deployments. Byzantine-robust algorithms, as the research by Fang et al. demonstrated, are more vulnerable to optimised attacks than previously believed.
The research community continues to develop new defences. A 2025 study proposed “shadow defense against gradient inversion attack,” using decoy gradients to obscure genuine updates. LSTM-based approaches attempt to detect malicious updates by analysing patterns across communication rounds. The FedMP algorithm combines multiple defensive techniques into a “multi-pronged defence” against Byzantine attacks.
But attackers are also advancing. Gradient-guided diffusion models achieve reconstruction quality that would have seemed impossible a few years ago. Adaptive attack strategies that vary the number of malicious clients per round prove more effective and harder to detect. The boundary between secure and insecure keeps shifting.
This dynamic suggests that privacy-preserving AI should not be understood as a solved problem but as an ongoing negotiation between attackers and defenders, with no permanent resolution in sight.
What Users Actually Want
Amid all the technical complexity, it is worth returning to the fundamental question: what do users actually want from privacy protection, and can federated learning and differential privacy deliver it?
Research suggests that user expectations are contextual and nuanced. People are more willing to share data with well-known, trusted entities than with unknown ones. They want personalised services but also want protection from misuse. They care more about some types of data than others, and their concerns vary by situation.
Privacy-preserving techniques address some of these concerns better than others. They reduce the risk of data breaches by not centralising sensitive information. They provide mathematical frameworks for limiting what can be inferred about individuals. They enable beneficial applications, such as medical AI or improved keyboard prediction, that might otherwise be impossible due to privacy constraints.
But they do not address the fundamental power imbalance between individuals and the organisations that deploy these systems. They do not give users meaningful control over how models trained on their data are used. They do not make privacy trade-offs transparent or negotiable. They replace visible data collection with invisible model training, which may reduce certain risks whilst obscuring others.
The privacy paradox literature suggests that many users make rational calculations based on perceived benefits and risks. But federated learning and differential privacy make those calculations harder, not easier. The average user cannot evaluate whether epsilon equals 2 provides adequate protection for their threat model. They cannot assess whether gradient inversion attacks pose a realistic risk in their context. They must simply trust that the deploying organisation has made these decisions competently and in good faith.
The Question That Matters
Will you feel safe sharing personal data as AI systems adopt federated learning and differential privacy? The honest answer is: it depends on what you mean by “safe.”
These techniques genuinely reduce certain privacy risks. They make centralised data breaches less catastrophic by keeping data distributed. They provide formal guarantees that limit what can be inferred about individuals, at least in theory. They enable beneficial applications that would otherwise founder on privacy concerns.
But they also create new vulnerabilities that researchers are only beginning to understand. Gradient inversion attacks can reconstruct sensitive data from model updates. Malicious participants can poison shared models. The privacy-utility trade-off means that stronger guarantees come at the cost of usefulness, a cost that often falls disproportionately on already marginalised populations.
Corporate incentives shape how these technologies are deployed. Companies that profit from data collection have reasons to adopt privacy-preserving techniques that maintain their business models whilst satisfying regulators and reassuring users. This is not necessarily malicious, but it is also not the same as prioritising user privacy above all else.
The gap between technical guarantees and user understanding remains vast. Few users can meaningfully evaluate privacy claims couched in mathematical parameters and threat models. The complexity of these systems may actually reduce accountability by making it harder to identify when privacy has been violated.
Perhaps most importantly, these techniques do not fundamentally change the relationship between individuals and the organisations that train AI on their data. They are tools that can be used for better or worse, depending on who deploys them and why. They are not a solution to the privacy problem so much as a new set of trade-offs to navigate.
The question is not whether federated learning and differential privacy make you safer, because the answer is nuanced and contextual. The question is whether you trust the organisations deploying these techniques to make appropriate decisions on your behalf, whether you believe the oversight mechanisms are adequate, and whether you accept the trade-offs inherent in the technology.
For some users, in some contexts, the answer will be yes. The ability to contribute to medical AI research without sharing raw health records, or to improve keyboard prediction without uploading every message, represents genuine progress. For others, the answer will remain no, because no amount of mathematical sophistication can substitute for genuine control over one's own data.
Privacy-preserving AI is neither panacea nor theatre. It is a set of tools with real benefits and real limitations, deployed by organisations with mixed motivations, in a regulatory environment that is still evolving. The honest assessment is that these techniques make some attacks harder and enable some attacks we have not yet fully understood. They reduce some risks whilst obscuring others. They represent progress, but not a destination.
As these technologies continue to develop, the most important thing users can do is maintain healthy scepticism, demand transparency about the specific techniques and parameters being used, and recognise that privacy in the age of AI requires ongoing vigilance rather than passive trust in technical solutions. The machines may be learning to protect your privacy, but whether they succeed depends on far more than the mathematics.
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Tim Green
UK-based Systems Theorist & Independent Technology Writer
Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.
His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.
ORCID: 0009-0002-0156-9795
Email: tim@smarterarticles.co.uk
