Definition
Federated Learning Security encompasses protections against threats in FL systems, where clients train local models on private data and share updates (e.g., gradients) with a central server for aggregation into a global model. Key risks include data poisoning (malicious updates degrade performance), model inversion (reconstructing private data from updates), membership inference (detecting if data was used), and backdoor attacks (embedding triggers for targeted misbehavior).
Security relies on threat models assuming honest-but-curious servers, malicious clients (Byzantine), or external eavesdroppers. Defenses enforce the CIA triad: confidentiality (homomorphic encryption, secure multi-party computation), integrity (robust aggregation like Krum), and availability (Byzantine-resilient protocols). FL variants: horizontal (shared features), vertical (shared samples), and transfer learning; amplify unique risks, necessitating hybrid defenses like differential privacy (DP) + secure aggregation.
Core Threats and Attack Vectors in FL Ecosystems
FL’s decentralized nature exposes multi-phase vulnerabilities: auditing (data tampering), training (update manipulation), and inference (model exploitation). Attackers exploit non-IID data, heterogeneous devices, and communication channels.
Threats span poisoning (e.g., label flipping reduces accuracy by 50-90%), inference (extracting training samples via gradient inversion), and inversion (reconstructing images from updates). Byzantine attacks simulate faulty clients (up to 49% in robust setups).
Eavesdropping on updates leaks via model memorization, as shown in NIST examples where digits/text are extracted post-training.
Poisoning attacks: Malicious clients inject faulty gradients, causing global model failure (e.g., targeted misclassification).
Privacy leakage: Gradient ascent reconstructs inputs; DL models memorize data, enabling extraction from parameters.
Byzantine/inference: Faulty updates or queries reveal membership (90%+ accuracy).
Communication risks: Man-in-the-middle tampers with updates; non-IID exacerbates convergence issues.
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Defenses: Balancing Privacy, Robustness, and Efficiency
Mitigations layer cryptographic primitives, statistical noise, and algorithmic safeguards across the FL lifecycle. DP adds calibrated noise to updates (ε=1-10 for utility-privacy trade-off); secure aggregation (e.g., SecAgg) masks individual contributions. Robust methods like median/Krum filter outliers (tolerate 40% malicious clients).
Cryptographic defenses shine in vertical/horizontal FL: homomorphic encryption (HE) computes on ciphertexts; secure multi-party computation (SMPC) enables threshold aggregation. Intel SGX/TEE isolates execution; blockchain decentralizes trust. Trade-offs: HE inflates compute 100-1000x, mitigated by hybrid DP+HE.
Robust aggregation (e.g., FedProx handles non-IID) and client vetting (reputation scores) counter poisoning. Post-training, model pruning/DP-SGD curbs memorization. Evaluations show a 20-30% accuracy drop under attacks, recoverable via defenses.
Differential Privacy (DP): Noise injection (σ=0.5-2) prevents inversion (90% defense efficacy).
Secure Aggregation: Threshold schemes hide updates (e.g., Bonawitz protocol).
Robust Optimizers: Trimmed mean/Krum reject 20-50% outliers.
TEE/HE/SMPC: Hardware enclaves or ciphertexts ensure confidentiality.
Blockchain: Decentralized FL verifies contributions.
Client Selection: Reputation-based auditing excludes malicious nodes.
Advanced Techniques and Emerging Challenges
FL security evolves with decentralized (blockchain-integrated) and heterogeneous setups, addressing scalability (1000+ clients) and quantum threats.
- GAN-based attacks: Generate stealthy poisons; countered by anomaly detection.
- Decentralized FL: Gossip protocols + DP resist single-point failures.
- Vertical FL: Entity resolution via HE prevents ID leaks.
- Quantum-resistant: Lattice-based crypto for post-quantum security.
- Evaluation metrics: Attack Success Rate (ASR<5%), Privacy Loss (ε<1), Utility (accuracy drop<10%).
- Open challenges: Non-IID robustness, communication efficiency (compress 90% updates), regulatory compliance (GDPR).
- Tools: OpenFL, Flower, TensorFlow Federated with built-in defenses.
Summary
Federated Learning Security fortifies distributed training against poisoning, inference, and Byzantine threats via DP, robust aggregation, and crypto (HE/SMPC/TEE), enabling secure collaboration in silos like healthcare/finance. While attacks extract data from updates/models, layered defenses maintain utility (accuracy>90%) with manageable overhead. Future-proofing demands quantum-safe, scalable protocols amid non-IID/heterogeneous challenges.