class SparseEncoder(nn.Module):
    def __init__(self, input_dim, latent_dim, sparsity_ratio=0.05):
        super().__init__()
        self.encoder = nn.Linear(input_dim, latent_dim)
        self.sparsity_ratio = sparsity_ratio

    def forward(self, x):
        # Dense encoding
        z = torch.relu(self.encoder(x))

        # Top-k sparsification: keep only top 5% activations
        k = max(1, int(self.sparsity_ratio * z.shape[-1]))
        threshold = torch.topk(z, k, dim=-1).values[:, -1:]
        mask = (z >= threshold).float()

        # Sparse latent representation
        return z * mask

def sparse_federated_update(local_model, global_model, device_data, sparsity_level=0.01):
    """
    Compute sparse gradient updates for federated learning.
    Only transmits the most significant gradient components.
    """
    # Local training on device
    local_weights = train_local(local_model, device_data, epochs=3)

    # Compute dense gradient
    dense_gradient = {
        name: local_weights[name] - global_model[name]
        for name in global_model.keys()
    }

    # Sparse compression via magnitude pruning
    sparse_updates = {}
    for name, grad in dense_gradient.items():
        # Keep only top-k% of gradient magnitudes
        k = max(1, int(sparsity_level * grad.numel()))
        flat_grad = grad.view(-1)

        if flat_grad.numel() > k:
            indices = torch.topk(flat_grad.abs(), k).indices
            sparse_updates[name] = {
                'indices': indices.cpu().numpy(),
                'values': flat_grad[indices].cpu().numpy(),
                'shape': grad.shape
            }
        else:
            sparse_updates[name] = {
                'indices': None,
                'values': flat_grad.cpu().numpy(),
                'shape': grad.shape
            }

    return sparse_updates

class CoastalVAE(nn.Module):
    """
    Lightweight VAE for coastal environmental state representation.
    Designed for 8-bit microcontrollers with <256KB RAM.
    """
    def __init__(self, input_dim=64, latent_dim=16):
        super().__init__()
        # Encoder: 64 -> 32 -> 16
        self.enc1 = nn.Linear(input_dim, 32)
        self.enc2 = nn.Linear(32, latent_dim * 2)  # mu and logvar

        # Decoder: 16 -> 32 -> 64
        self.dec1 = nn.Linear(latent_dim, 32)
        self.dec2 = nn.Linear(32, input_dim)

        # Quantization for 8-bit deployment
        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()

    def encode(self, x):
        x = self.quant(x)
        h = torch.relu(self.enc1(x))
        params = self.enc2(h)
        mu, logvar = params.chunk(2, dim=-1)
        return mu, logvar

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std

    def forward(self, x):
        mu, logvar = self.encode(x)
        z = self.reparameterize(mu, logvar)
        h = torch.relu(self.dec1(z))
        recon = torch.sigmoid(self.dec2(h))
        return recon, mu, logvar, z

class ClusterAwareAggregator:
    def __init__(self, num_clusters=3, similarity_threshold=0.7):
        self.num_clusters = num_clusters
        self.similarity_threshold = similarity_threshold
        self.cluster_centers = None
        self.device_clusters = {}

    def aggregate(self, device_updates, device_metadata):
        """
        Cluster devices by their data distribution characteristics
        and perform cluster-specific model aggregation.
        """
        # Extract representation statistics
        rep_stats = []
        for device_id, update in device_updates.items():
            # Use update statistics as proxy for data distribution
            stats = self._extract_distribution_stats(update)
            rep_stats.append(stats)

        # Cluster devices
        from sklearn.cluster import KMeans
        kmeans = KMeans(n_clusters=self.num_clusters)
        clusters = kmeans.fit_predict(rep_stats)

        # Aggregate within clusters
        cluster_models = {}
        for cluster_id in range(self.num_clusters):
            cluster_devices = [
                device_id for device_id, c in zip(device_updates.keys(), clusters)
                if c == cluster_id
            ]

            if cluster_devices:
                # Weighted average of updates within cluster
                cluster_model = self._weighted_average(
                    [device_updates[d] for d in cluster_devices]
                )
                cluster_models[cluster_id] = cluster_model

        return cluster_models, clusters

def quantum_inspired_gradient_selection(gradients, num_selected):
    """
    Select gradient components using quantum-inspired amplitude amplification.
    Uses Grover-like search to find the most impactful updates.
    """
    # Normalize gradients to create probability distribution
    grad_norms = torch.norm(gradients, dim=-1)
    probabilities = grad_norms / grad_norms.sum()

    # Amplitude amplification: iteratively boost high-probability components
    for _ in range(3):  # Amplification rounds
        # Square the probabilities (amplitude amplification)
        probabilities = probabilities ** 2
        probabilities = probabilities / probabilities.sum()

    # Sample according to amplified distribution
    selected_indices = torch.multinomial(probabilities, num_selected, replacement=False)

    # Create sparse mask
    mask = torch.zeros_like(gradients)
    mask[selected_indices] = 1.0

    return gradients * mask

class AutonomousDeploymentAgent:
    """
    Agentic AI system for managing federated learning across
    distributed coastal sensors.
    """
    def __init__(self, coordinator_address, device_registry):
        self.coordinator = coordinator_address
        self.devices = device_registry
        self.learning_state = {}
        self.policy_network = self._init_policy_network()

    def decide_learning_schedule(self, device_status):
        """
        Use reinforcement learning to decide when to initiate
        federated learning rounds based on device availability,
        battery levels, and data importance.
        """
        state = self._encode_device_state(device_status)

        # Policy network outputs action probabilities
        # Actions: (start_round, skip_round, adjust_sparsity)
        action_probs = self.policy_network(state)
        action = torch.argmax(action_probs).item()

        return self._map_action_to_learning_params(action, device_status)

    def handle_communication_failure(self, device_id, failure_type):
        """
        Adaptive response to communication failures.
        Implements exponential backoff with environmental awareness.
        """
        # Check if failure correlates with environmental conditions
        env_context = self._get_environmental_context(device_id)

        if failure_type == 'timeout':
            # Increase sparsity for next communication attempt
            self.learning_state[device_id]['sparsity'] *= 0.5
        elif failure_type == 'corruption':
            # Trigger local model rollback and retraining
            self._rollback_and_retrain(device_id)

        # Schedule retry based on predicted connectivity windows
        retry_time = self._predict_next_connectivity_window(device_id)
        return retry_time

def encode_sparse_update(update):
    """
    Encode sparse gradient updates into minimal binary format.
    Uses delta encoding for index compression and variable-length
    integer encoding for values.
    """
    import struct

    encoded = bytearray()
    for layer_name, layer_update in update.items():
        # Layer header
        encoded.extend(struct.pack('B', len(layer_name)))
        encoded.extend(layer_name.encode())

        if layer_update['indices'] is None:
            # Dense layer: just pack all values as float16
            values = layer_update['values'].astype(np.float16)
            encoded.extend(struct.pack('<H', len(values)))
            encoded.extend(values.tobytes())
        else:
            # Sparse layer: delta-encoded indices + float16 values
            indices = layer_update['indices']
            deltas = np.diff(np.concatenate([[0], indices]))
            encoded.extend(struct.pack('<H', len(indices)))
            encoded.extend(deltas.astype(np.uint16).tobytes())

            values = layer_update['values'].astype(np.float16)
            encoded.extend(values.tobytes())

    return bytes(encoded)

python
class ContinualCoastalLearner:
    def __init__(self, model, importance_lambda=100):
        self.model = model
        self.fisher_matrix = None
        self.importance_lambda = importance_lambda

    def update_fisher(self, data_loader):
        """
        Compute Fisher information matrix to identify important weights.
        Called periodically to track which parameters are crucial
        for previously learned tasks.
        """
        self.model.eval()
        fisher = {}
        for name, param in self.model.named_parameters():
            fisher[name] = torch.zeros_like(param)

        for batch in data_loader:
            self.model.zero_grad()
            output = self.model(batch)
            loss = self._compute_loss(output)
            loss.backward()