BAFFLE : Blockchain Based Aggregator Free Federated Learning
This addresses the operational constraints of centralized aggregators in federated learning, offering a decentralized alternative, though it appears incremental as it adapts existing blockchain and federated learning concepts.
The paper tackles the problem of requiring a centralized aggregator in federated learning by introducing BAFFLE, a blockchain-based aggregator-free federated learning environment that uses smart contracts for coordination. The results show that BAFFLE significantly reduces gas costs on a private Ethereum network compared to aggregator-based methods while achieving similar accuracy and high scalability.
A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to maintain and update the global model. However, in many cases orchestrating a centralized aggregator might be infeasible due to numerous operational constraints. In this paper, we introduce BAFFLE, an aggregator free, blockchain driven, FL environment that is inherently decentralized. BAFFLE leverages Smart Contracts (SC) to coordinate the round delineation, model aggregation and update tasks in FL. BAFFLE boosts computational performance by decomposing the global parameter space into distinct chunks followed by a score and bid strategy. In order to characterize the performance of BAFFLE, we conduct experiments on a private Ethereum network and use the centralized and aggregator driven methods as our benchmark. We show that BAFFLE significantly reduces the gas costs for FL on the blockchain as compared to a direct adaptation of the aggregator based method. Our results also show that BAFFLE achieves high scalability and computational efficiency while delivering similar accuracy as the benchmark methods.