Federated Learning¶

from qmemetic_ai.federation.client import FederatedClient
from qmemetic_ai.federation.protocols import FederatedProtocol

# Initialize federated client
fed_client = FederatedClient(
    node_id="university_research_lab",
    config={
        "discovery_port": 8080,
        "coordination_port": 8081,
        "max_peers": 20,
        "trust_threshold": 0.7,
        "privacy_level": "high",
        "bandwidth_limit": "10MB/s"
    }
)

# Configure federation protocols
protocol = FederatedProtocol(
    encryption_enabled=True,
    differential_privacy=True,
    consensus_algorithm="proof_of_contribution"
)

fed_client.set_protocol(protocol)

# Register with the federated network
registration_result = await fed_client.register_with_network(
    capabilities={
        "computational_power": "high",
        "storage_capacity": "1TB",
        "specializations": ["AI", "quantum_computing", "biology"],
        "available_hours": "24/7"
    },
    contact_info={
        "organization": "University Research Lab",
        "contact_email": "federated@university.edu",
        "geographic_region": "North America"
    }
)

if registration_result["success"]:
    print(f"Registered as node: {registration_result['node_id']}")
    print(f"Network address: {registration_result['network_address']}")

# Discover other nodes in the network
peers = await fed_client.discover_peers(
    filters={
        "specializations": ["AI", "machine_learning"],
        "min_trust_score": 0.8,
        "geographic_preference": "same_region",
        "active_within_hours": 24
    }
)

print(f"Discovered {len(peers)} compatible peers:")
for peer in peers:
    print(f"  {peer.node_id}: {peer.specializations} (trust: {peer.trust_score:.2f})")

async def federated_evolution_session():
    """Run distributed evolution across multiple nodes."""

    # Prepare local meme population
    local_memes = [
        engine.create_meme("Local research insight A"),
        engine.create_meme("Local research insight B"),
        engine.create_meme("Local research insight C")
    ]

    # Configure federated evolution parameters
    fed_params = {
        "generations": 10,
        "migration_rate": 0.2,      # 20% of population migrates each generation
        "migration_interval": 2,     # Migrate every 2 generations
        "consensus_threshold": 0.8,  # 80% agreement for accepting mutations
        "privacy_budget": 1.0        # Differential privacy budget
    }

    # Start federated evolution
    evolution_session = await fed_client.start_federated_evolution(
        local_population=local_memes,
        parameters=fed_params,
        participating_nodes=selected_peers
    )

    # Monitor evolution progress
    async for generation_result in evolution_session:
        print(f"Generation {generation_result.generation}:")
        print(f"  Local fitness: {generation_result.local_fitness:.3f}")
        print(f"  Global fitness: {generation_result.global_fitness:.3f}")
        print(f"  Received memes: {len(generation_result.received_memes)}")
        print(f"  Sent memes: {len(generation_result.sent_memes)}")

    return evolution_session.final_population

# Run federated evolution
evolved_population = await federated_evolution_session()

class MigrationStrategy:
    """Manage meme migration between federated nodes."""

    def __init__(self, strategy_type="fitness_based"):
        self.strategy_type = strategy_type
        self.migration_history = []

    def select_migrants(self, population, migration_rate=0.2):
        """Select memes for migration to other nodes."""
        num_migrants = int(len(population) * migration_rate)

        if self.strategy_type == "fitness_based":
            # Migrate highest fitness memes
            sorted_pop = sorted(population, key=lambda m: m.fitness, reverse=True)
            migrants = sorted_pop[:num_migrants]

        elif self.strategy_type == "diversity_based":
            # Migrate most diverse memes
            migrants = self.select_diverse_memes(population, num_migrants)

        elif self.strategy_type == "random":
            # Random migration
            migrants = random.sample(population, num_migrants)

        elif self.strategy_type == "novelty_based":
            # Migrate novel memes that might be valuable elsewhere
            migrants = self.select_novel_memes(population, num_migrants)

        return migrants

    def receive_migrants(self, local_population, incoming_memes):
        """Integrate incoming memes into local population."""
        # Apply privacy filters
        filtered_memes = self.apply_privacy_filters(incoming_memes)

        # Validate memes for compatibility
        validated_memes = self.validate_incoming_memes(filtered_memes)

        # Integrate into local population
        integrated_population = self.integrate_memes(local_population, validated_memes)

        return integrated_population

# Use migration strategy
migration_strategy = MigrationStrategy(strategy_type="diversity_based")
fed_client.set_migration_strategy(migration_strategy)

class DifferentialPrivacyManager:
    """Implement differential privacy for federated learning."""

    def __init__(self, epsilon=1.0, delta=1e-5):
        self.epsilon = epsilon  # Privacy budget
        self.delta = delta      # Failure probability
        self.noise_scale = 1.0 / epsilon

    def add_noise_to_meme(self, meme):
        """Add calibrated noise to preserve privacy."""
        # Clone meme to avoid modifying original
        private_meme = meme.copy()

        # Add noise to numerical features
        if hasattr(private_meme, 'vector'):
            noise = np.random.laplace(0, self.noise_scale, len(private_meme.vector))
            private_meme.vector = private_meme.vector + noise

        # Add noise to metadata
        if hasattr(private_meme.metadata, 'fitness'):
            noise = np.random.laplace(0, self.noise_scale)
            private_meme.metadata.fitness += noise

        return private_meme

    def privacy_budget_check(self, requested_operations):
        """Check if privacy budget allows requested operations."""
        total_cost = sum(op.privacy_cost for op in requested_operations)
        return total_cost <= self.epsilon

    def aggregate_with_privacy(self, local_gradients, global_model):
        """Aggregate model updates with differential privacy."""
        # Add noise to gradients before aggregation
        noisy_gradients = []
        for gradient in local_gradients:
            noise = np.random.laplace(0, self.noise_scale, gradient.shape)
            noisy_gradients.append(gradient + noise)

        # Aggregate noisy gradients
        aggregated_gradient = np.mean(noisy_gradients, axis=0)

        # Update global model
        updated_model = global_model + aggregated_gradient
        return updated_model

# Apply differential privacy
privacy_manager = DifferentialPrivacyManager(epsilon=1.0)
fed_client.set_privacy_manager(privacy_manager)

class SecureComputationProtocol:
    """Implement secure multi-party computation for federated operations."""

    def __init__(self, num_parties, threshold=None):
        self.num_parties = num_parties
        self.threshold = threshold or (num_parties // 2 + 1)
        self.secret_shares = {}

    def secret_share(self, value, party_id):
        """Create secret shares of a value."""
        # Generate random polynomial coefficients
        coefficients = [value] + [random.randint(0, 2**31 - 1) for _ in range(self.threshold - 1)]

        # Generate shares for each party
        shares = {}
        for i in range(1, self.num_parties + 1):
            share_value = sum(coeff * (i ** j) for j, coeff in enumerate(coefficients)) % (2**31)
            shares[i] = share_value

        return shares

    def reconstruct_secret(self, shares):
        """Reconstruct secret from shares using Lagrange interpolation."""
        if len(shares) < self.threshold:
            raise ValueError("Insufficient shares for reconstruction")

        # Lagrange interpolation at x=0
        secret = 0
        for i, (x_i, y_i) in enumerate(shares.items()):
            # Calculate Lagrange basis polynomial
            basis = 1
            for j, (x_j, _) in enumerate(shares.items()):
                if i != j:
                    basis *= (-x_j) / (x_i - x_j)
            secret += y_i * basis

        return int(secret) % (2**31)

    def secure_aggregation(self, local_values, participant_ids):
        """Perform secure aggregation of values from multiple parties."""
        # Each party creates secret shares of their value
        all_shares = {}
        for party_id, value in zip(participant_ids, local_values):
            shares = self.secret_share(value, party_id)
            all_shares[party_id] = shares

        # Aggregate shares
        aggregated_shares = {}
        for party_id in participant_ids:
            aggregated_shares[party_id] = sum(
                shares[party_id] for shares in all_shares.values()
            )

        # Reconstruct aggregated result
        aggregated_result = self.reconstruct_secret(aggregated_shares)
        return aggregated_result

# Use secure computation
secure_protocol = SecureComputationProtocol(num_parties=5)
fed_client.set_secure_protocol(secure_protocol)

class ProofOfContribution:
    """Consensus mechanism based on computational contributions."""

    def __init__(self):
        self.contribution_history = {}
        self.reputation_scores = {}

    def calculate_contribution_score(self, node_id, contributions):
        """Calculate contribution score for a node."""
        score = 0.0

        # Computational contribution (CPU hours, evolution cycles)
        computational_score = contributions.get("computational_hours", 0) * 0.3

        # Data contribution (unique memes, dataset size)
        data_score = contributions.get("unique_memes", 0) * 0.2

        # Quality contribution (meme fitness improvements)
        quality_score = contributions.get("fitness_improvements", 0) * 0.3

        # Network contribution (peer connections, uptime)
        network_score = contributions.get("network_uptime", 0) * 0.2

        total_score = computational_score + data_score + quality_score + network_score

        # Update historical contribution
        if node_id not in self.contribution_history:
            self.contribution_history[node_id] = []
        self.contribution_history[node_id].append(total_score)

        return total_score

    def update_reputation(self, node_id, contribution_score):
        """Update node reputation based on contributions."""
        current_reputation = self.reputation_scores.get(node_id, 0.5)

        # Exponential moving average for reputation
        alpha = 0.1  # Learning rate
        new_reputation = alpha * contribution_score + (1 - alpha) * current_reputation

        self.reputation_scores[node_id] = max(0.0, min(1.0, new_reputation))

        return self.reputation_scores[node_id]

    def select_validators(self, num_validators=5):
        """Select validator nodes based on reputation and contribution."""
        # Sort nodes by reputation score
        sorted_nodes = sorted(
            self.reputation_scores.items(),
            key=lambda x: x[1],
            reverse=True
        )

        # Select top contributors as validators
        validators = [node_id for node_id, _ in sorted_nodes[:num_validators]]
        return validators

# Implement proof of contribution
poc_consensus = ProofOfContribution()
fed_client.set_consensus_mechanism(poc_consensus)

class ByzantineFaultTolerantConsensus:
    """BFT consensus for federated memetic computing."""

    def __init__(self, num_nodes, max_byzantine_nodes=None):
        self.num_nodes = num_nodes
        self.max_byzantine_nodes = max_byzantine_nodes or (num_nodes - 1) // 3
        self.view_number = 0
        self.sequence_number = 0
        self.message_log = []

    async def propose_meme_update(self, meme_update, proposer_id):
        """Propose a meme update using PBFT protocol."""
        proposal = {
            "type": "prepare",
            "view": self.view_number,
            "sequence": self.sequence_number,
            "meme_update": meme_update,
            "proposer": proposer_id,
            "timestamp": time.time()
        }

        # Phase 1: Prepare
        prepare_responses = await self.broadcast_prepare(proposal)

        if len(prepare_responses) >= 2 * self.max_byzantine_nodes + 1:
            # Phase 2: Commit
            commit_responses = await self.broadcast_commit(proposal)

            if len(commit_responses) >= 2 * self.max_byzantine_nodes + 1:
                # Apply meme update
                await self.apply_meme_update(meme_update)
                self.sequence_number += 1
                return True

        return False

    async def validate_meme_update(self, meme_update):
        """Validate proposed meme update."""
        validation_checks = [
            self.check_meme_integrity(meme_update),
            self.check_fitness_improvement(meme_update),
            self.check_privacy_compliance(meme_update),
            self.check_consensus_rules(meme_update)
        ]

        return all(validation_checks)

# Use Byzantine fault tolerance
bft_consensus = ByzantineFaultTolerantConsensus(num_nodes=10)
fed_client.set_bft_consensus(bft_consensus)

async def cross_domain_transfer():
    """Transfer knowledge between different research domains."""

    # Define source and target domains
    source_domain = "computer_science"
    target_domain = "biology"

    # Find domain-specific federated nodes
    source_nodes = await fed_client.find_nodes_by_domain(source_domain)
    target_nodes = await fed_client.find_nodes_by_domain(target_domain)

    # Create transfer learning session
    transfer_session = await fed_client.create_transfer_session(
        source_nodes=source_nodes,
        target_nodes=target_nodes,
        transfer_method="domain_adaptation"
    )

    # Extract transferable knowledge patterns
    transferable_patterns = await transfer_session.extract_patterns(
        min_relevance=0.6,
        max_domain_gap=0.8
    )

    print(f"Found {len(transferable_patterns)} transferable patterns")

    # Apply domain adaptation
    adapted_patterns = await transfer_session.adapt_patterns(
        patterns=transferable_patterns,
        target_domain_context=target_domain
    )

    return adapted_patterns

# Execute cross-domain transfer
transferred_knowledge = await cross_domain_transfer()

class ResearchCollaboration:
    """Facilitate collaborative research through federated networks."""

    def __init__(self, research_topic):
        self.research_topic = research_topic
        self.participating_institutions = []
        self.shared_hypotheses = []
        self.collaborative_experiments = []

    async def form_research_consortium(self, topic_keywords):
        """Form consortium of institutions researching similar topics."""
        # Find institutions with relevant expertise
        relevant_nodes = await fed_client.search_nodes(
            keywords=topic_keywords,
            expertise_threshold=0.7
        )

        # Send collaboration invitations
        invitations = []
        for node in relevant_nodes:
            invitation = await self.send_collaboration_invite(
                node_id=node.node_id,
                research_topic=self.research_topic,
                proposed_contributions=self.define_contributions(node)
            )
            invitations.append(invitation)

        # Process responses
        confirmed_participants = []
        for invitation in invitations:
            response = await invitation.get_response(timeout=7200)  # 2 hours
            if response.accepted:
                confirmed_participants.append(response.node_id)

        self.participating_institutions = confirmed_participants
        return confirmed_participants

    async def collaborative_hypothesis_generation(self):
        """Generate research hypotheses collaboratively."""
        # Collect local hypotheses from each institution
        local_hypotheses = []
        for institution in self.participating_institutions:
            hypotheses = await fed_client.request_hypotheses(
                node_id=institution,
                research_topic=self.research_topic
            )
            local_hypotheses.extend(hypotheses)

        # Merge and evolve hypotheses collaboratively
        merged_hypotheses = await self.merge_hypotheses(local_hypotheses)
        evolved_hypotheses = await fed_client.collaborative_evolution(
            population=merged_hypotheses,
            participants=self.participating_institutions,
            generations=5
        )

        self.shared_hypotheses = evolved_hypotheses
        return evolved_hypotheses

    async def distribute_experiments(self, experiment_designs):
        """Distribute experiments across participating institutions."""
        experiment_assignments = {}

        for experiment in experiment_designs:
            # Find best-suited institution for each experiment
            best_match = await self.find_best_institution(
                experiment=experiment,
                criteria=["expertise", "resources", "availability"]
            )

            if best_match:
                experiment_assignments[experiment.id] = best_match
                await self.assign_experiment(experiment, best_match)

        return experiment_assignments

# Create research collaboration
collaboration = ResearchCollaboration("AI safety in autonomous systems")
consortium = await collaboration.form_research_consortium(
    ["AI safety", "autonomous systems", "ethical AI"]
)

class FederatedLoadBalancer:
    """Balance computational load across federated nodes."""

    def __init__(self):
        self.node_capacities = {}
        self.current_loads = {}
        self.load_history = {}

    def update_node_capacity(self, node_id, capacity_info):
        """Update node capacity information."""
        self.node_capacities[node_id] = {
            "cpu_cores": capacity_info["cpu_cores"],
            "memory_gb": capacity_info["memory_gb"],
            "storage_gb": capacity_info["storage_gb"],
            "network_bandwidth": capacity_info["network_bandwidth"],
            "gpu_available": capacity_info.get("gpu_available", False)
        }

    def calculate_load_score(self, node_id):
        """Calculate current load score for a node."""
        if node_id not in self.current_loads:
            return 0.0

        load = self.current_loads[node_id]
        capacity = self.node_capacities.get(node_id, {})

        if not capacity:
            return 1.0  # Unknown capacity, assume fully loaded

        # Calculate relative load
        cpu_load = load.get("cpu_usage", 0) / capacity.get("cpu_cores", 1)
        memory_load = load.get("memory_usage", 0) / capacity.get("memory_gb", 1)

        # Weighted average
        load_score = 0.6 * cpu_load + 0.4 * memory_load
        return min(load_score, 1.0)

    def select_optimal_nodes(self, task_requirements, num_nodes=3):
        """Select optimal nodes for task distribution."""
        candidate_nodes = []

        for node_id, capacity in self.node_capacities.items():
            # Check if node meets requirements
            if self.meets_requirements(capacity, task_requirements):
                load_score = self.calculate_load_score(node_id)
                suitability_score = self.calculate_suitability(capacity, task_requirements)

                combined_score = 0.7 * (1 - load_score) + 0.3 * suitability_score
                candidate_nodes.append((node_id, combined_score))

        # Sort by combined score and select top nodes
        candidate_nodes.sort(key=lambda x: x[1], reverse=True)
        selected_nodes = [node_id for node_id, _ in candidate_nodes[:num_nodes]]

        return selected_nodes

# Implement load balancing
load_balancer = FederatedLoadBalancer()
fed_client.set_load_balancer(load_balancer)

class BandwidthOptimizer:
    """Optimize network bandwidth usage in federated operations."""

    def __init__(self):
        self.compression_algorithms = ["gzip", "lz4", "zstd"]
        self.bandwidth_monitors = {}

    def compress_meme_data(self, meme_data, algorithm="zstd"):
        """Compress meme data for efficient transmission."""
        import zstandard as zstd

        if algorithm == "zstd":
            compressor = zstd.ZstdCompressor()
            compressed_data = compressor.compress(meme_data.encode())
        elif algorithm == "gzip":
            import gzip
            compressed_data = gzip.compress(meme_data.encode())
        elif algorithm == "lz4":
            import lz4.frame
            compressed_data = lz4.frame.compress(meme_data.encode())

        compression_ratio = len(meme_data) / len(compressed_data)
        return compressed_data, compression_ratio

    def adaptive_batch_sizing(self, available_bandwidth, data_size):
        """Adaptively determine optimal batch size for data transmission."""
        # Target transmission time: 10 seconds per batch
        target_time = 10.0  # seconds

        # Calculate optimal batch size
        optimal_batch_size = int(available_bandwidth * target_time)

        # Ensure batch size is reasonable
        min_batch_size = 1024  # 1 KB
        max_batch_size = 10 * 1024 * 1024  # 10 MB

        batch_size = max(min_batch_size, min(optimal_batch_size, max_batch_size))

        # Calculate number of batches
        num_batches = (data_size + batch_size - 1) // batch_size

        return batch_size, num_batches

    def prioritize_data_transmission(self, data_queue):
        """Prioritize data transmission based on importance."""
        # Priority factors
        priorities = {
            "critical_updates": 1.0,
            "evolution_results": 0.8,
            "meme_migrations": 0.6,
            "routine_sync": 0.4,
            "backup_data": 0.2
        }

        # Sort queue by priority
        prioritized_queue = sorted(
            data_queue,
            key=lambda item: priorities.get(item.type, 0.3),
            reverse=True
        )

        return prioritized_queue

# Use bandwidth optimization
bandwidth_optimizer = BandwidthOptimizer()
fed_client.set_bandwidth_optimizer(bandwidth_optimizer)

class FederationAnalytics:
    """Analytics and monitoring for federated operations."""

    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.performance_tracker = PerformanceTracker()

    def generate_network_health_report(self):
        """Generate comprehensive network health report."""
        report = {
            "network_overview": {
                "total_nodes": len(self.active_nodes),
                "online_nodes": len(self.get_online_nodes()),
                "network_uptime": self.calculate_network_uptime(),
                "average_latency": self.calculate_average_latency()
            },
            "evolution_performance": {
                "completed_sessions": self.count_completed_sessions(),
                "average_convergence_time": self.calculate_convergence_time(),
                "fitness_improvements": self.calculate_fitness_improvements(),
                "successful_migrations": self.count_successful_migrations()
            },
            "resource_utilization": {
                "total_cpu_hours": self.sum_cpu_usage(),
                "total_bandwidth_used": self.sum_bandwidth_usage(),
                "storage_efficiency": self.calculate_storage_efficiency(),
                "cost_effectiveness": self.calculate_cost_effectiveness()
            },
            "security_metrics": {
                "privacy_violations": self.count_privacy_violations(),
                "consensus_failures": self.count_consensus_failures(),
                "byzantine_incidents": self.count_byzantine_incidents(),
                "data_integrity_score": self.calculate_integrity_score()
            }
        }

        return report

    def visualize_network_topology(self):
        """Create interactive visualization of network topology."""
        import networkx as nx
        import plotly.graph_objects as go

        # Create network graph
        G = nx.Graph()

        # Add nodes
        for node_id, node_info in self.active_nodes.items():
            G.add_node(node_id, **node_info)

        # Add edges (connections)
        for connection in self.active_connections:
            G.add_edge(
                connection.node_a,
                connection.node_b,
                weight=connection.strength
            )

        # Calculate layout
        pos = nx.spring_layout(G)

        # Create visualization
        node_trace = go.Scatter(
            x=[pos[node][0] for node in G.nodes()],
            y=[pos[node][1] for node in G.nodes()],
            mode='markers+text',
            text=[node for node in G.nodes()],
            textposition="middle center",
            hovertemplate="<b>%{text}</b><br>Connections: %{marker.size}<extra></extra>",
            marker=dict(
                size=[G.degree(node) * 3 for node in G.nodes()],
                color=[G.degree(node) for node in G.nodes()],
                colorscale='Viridis',
                showscale=True
            )
        )

        return {"nodes": node_trace, "layout": pos}

# Create analytics dashboard
analytics = FederationAnalytics()
health_report = analytics.generate_network_health_report()
network_viz = analytics.visualize_network_topology()