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Federated Learning (FL) is a popular approach for distributed deep learningthat prevents the pooling of large amounts of data in a central server. FLrelies on clients to update a global model using their local datasets.Classical FL algorithms use a central federator that, for each training round,waits for all clients to send their model updates before aggregating them. Inpractical deployments, clients might have different computing powers andnetwork capabilities, which might lead slow clients to become performancebottlenecks. Previous works have suggested to use a deadline for each learninground so that the federator ignores the late updates of slow clients, or sothat clients send partially trained models before the deadline. To speed up thetraining process, we instead propose Aergia, a novel approach where slowclients (i) freeze the part of their model that is the most computationallyintensive to train; (ii) train the unfrozen part of their model; and (iii)offload the training of the frozen part of their model to a faster client thattrains it using its own dataset. The offloading decisions are orchestrated bythe federator based on the training speed that clients report and on thesimilarities between their datasets, which are privately evaluated thanks to atrusted execution environment. We show through extensive experiments thatAergia maintains high accuracy and significantly reduces the training timeunder heterogeneous settings by up to 27% and 53% compared to FedAvg and TiFL,respectively.

Aergia: Leveraging Heterogeneity in Federated Learning Systems