Karthick Seshadri

Similarity based federated learning for heterogeneous data

This work proposes a similarity-based algorithm, FedKNN, which utilizes similarity between clients as part of the aggregation and restricts aggregation to the most similar neighborhood of the client, and represents the client networks as graph and shows that through similar neighborhood aggregation clients can benefit from the collective knowledge of the network and have flexibility in adapting to data drift.