Manuel Fernández-Veiga

Privacy-Preserving and Verifiable Approximate Distributed Coded Computing

This paper proposes a model-agnostic framework for adversary-resistant distributed learning that jointly addresses privacy preservation and malicious behavior across both federated and decentralized settings and empirically demonstrates that the combination of GPBACC with robust aggregation and verification mechanisms significantly reduces privacy leakage and improves resilience against active adversaries.