  In federated learning (FL), multiple clients collaborate to train machine
learning models together while keeping their data decentralized. Through
utilizing more training data, FL suffers from the potential negative transfer
problem: the global FL model may even perform worse than the models trained
with local data only. In this paper, we propose FedCollab, a novel FL framework
that alleviates negative transfer by clustering clients into non-overlapping
coalitions based on their distribution distances and data quantities. As a
result, each client only collaborates with the clients having similar data
distributions, and tends to collaborate with more clients when it has less
data. We evaluate our framework with a variety of datasets, models, and types
of non-IIDness. Our results demonstrate that FedCollab effectively mitigates
negative transfer across a wide range of FL algorithms and consistently
outperforms other clustered FL algorithms.
