byzantine-robust federated learning

**Byzantine-Robust Federated Learning** is a **federated learning framework designed to tolerate arbitrary malicious behavior from a fraction of participants** — ensuring that the global model converges correctly even when some clients send arbitrary, adversarial gradient updates. **Byzantine Threat Model** - **Byzantine Clients**: Can send any gradient update — random, adversarial, or strategically crafted. - **Fraction**: Typically assume $f < n/3$ or $f < n/2$ Byzantine clients (depending on the algorithm). - **Goal**: The global model should converge as if the Byzantine clients didn't exist. - **No Detection**: Byzantine-robust algorithms don't detect malicious clients — they ensure convergence despite them. **Why It Matters** - **Multi-Party Trust**: When multiple organizations collaborate, trust cannot be assumed — Byzantine robustness provides guarantees. - **Fault Tolerance**: Byzantine robustness also handles faulty (non-malicious) clients with software bugs or hardware failures. - **Theory**: Formal convergence guarantees under Byzantine threat models. **Byzantine-Robust FL** is **learning despite sabotage** — provably correct federated training even when some participants are adversarial or faulty.