Aggregator-Free Federated Learning is a term used in the context of federated machine learning, which is a privacy-preserving approach to train machine learning models across multiple decentralized devices or servers while keeping the data localized and secure.

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BAFFLE: Decentralized Blockchain based Aggregator-Free Federated Learning

BAFFLE: Decentralized Blockchain based Aggregator-Free Federated Learning 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.