  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.
