Trust-Aware Topology Learning for Dynamic Decentralized Federated Learning under Adversaries
This work shows that in decentralized learning, adversaries can attack the collaboration topology itself, not just model updates, and introduces DMTT, a trust-screened aggregation protocol that provably bounds Byzantine influence while empirically matching no-attack accuracy across mobile sensing benchmarks where existing robust aggregators collapse.