  Classical federated learning approaches yield significant performance
degradation in the presence of Non-IID data distributions of participants. When
the distribution of each local dataset is highly different from the global one,
the local objective of each client will be inconsistent with the global optima
which incur a drift in the local updates. This phenomenon highly impacts the
performance of clients. This is while the primary incentive for clients to
participate in federated learning is to obtain better personalized models. To
address the above-mentioned issue, we present a new algorithm, FLIS, which
groups the clients population in clusters with jointly trainable data
distributions by leveraging the inference similarity of clients' models. This
framework captures settings where different groups of users have their own
objectives (learning tasks) but by aggregating their data with others in the
same cluster (same learning task) to perform more efficient and personalized
federated learning. We present experimental results to demonstrate the benefits
of FLIS over the state-of-the-art benchmarks on CIFAR-100/10, SVHN, and FMNIST
datasets. Our code is available at https://github.com/MMorafah/FLIS.
