A Distributed Consensus Protocol for Sustainable Federated Learning

The most significant challenge of our time is global warming, it impacts every area of our lives. This study was motivated by the observation that to train Artificial Intelligence and Machine learning (AI/ML) algorithms result in staggering carbon footprints. Moreover, centralized implementations are becoming a bottleneck of several AI/ML applications that needs frequent retraining and low latency responses. To overcome the limitations of a centralized ML research community has proposed Federated Learning, a technique used to train AI/ML algorithms in a distributed fashion. There has been significant previous work to reduce power consumption by adopting efficient hardware techniques; while such techniques yield large savings, they are not focusing on distributed learning. We propose an Energy-efficient Consensus Protocol (EECP) for sustainable Federated Learning. Our protocol iterates over the bidding phase and agreement (or consensus) phase by only exchanging bids and a few other policy-driven information with neighbor workers. Our simulations show significant energy savings of up to 22.7% with respect to our benchmark.