FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution
This addresses the problem of inefficient and non-personalized models for clients in federated learning with non-IID data, representing an incremental improvement over existing clustering methods.
The paper tackles performance degradation in federated learning due to non-IID data distributions by proposing FLIS, which clusters clients based on inference similarity to improve personalization, achieving better results than state-of-the-art benchmarks on datasets like CIFAR-100/10, SVHN, and FMNIST.
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.