Privacy-Preserving Decentralized Aggregation for Federated Learning

In this paper, we develop a privacy-preserving decentralized aggregation protocol for federated learning. We formulate the distributed aggregation protocol with the Alternating Direction Method of Multiplier (ADMM) algorithm and examine its privacy challenges. Unlike prior works that use differential privacy or homomorphic encryption for privacy, we develop a protocol that controls communication a...