Self-Creating Random Walks for Decentralized Learning under Pac-Man Attacks

Jul 14, 2026

Researchers introduce the CREATE-IF-LATE (CIL) algorithm to address 'Pac-Man' attacks in decentralized learning, where malicious nodes can terminate random walks and disrupt learning. The CIL algorithm enables self-creating random walks, ensuring that the random walk population does not go extinct, remains bounded, and that stochastic gradient descent converges with a quantifiable deviation even under attack. Empirical results on synthetic and benchmark datasets support the theoretical guarantees.

Why it matters: This work provides a novel, decentralized defense against a stealthy adversarial threat in distributed learning systems, helping to maintain robust learning progress without centralized control.

Full story at: arXiv Multiagent Systems ↗