Efficient and Secure Federated Learning with Ensemble of One-Layer Neural Networks

Abstract

[Abstract]: In this work, we propose a Federated Learning (FL) method combining an ensemble of one-layer neural networks whose optimal parameters can be obtained through a non-iterative procedure. Therefore, unlike most state-of-the-art methods, the collaborative global model can be obtained using a single round of communication between all clients of the federated scheme. It presents a computationally efficient and incremental batch aggregation process that suits the needs of a realistic federated scenario, simplifying the management of the federated training process. The model provides the same performance in identically and non-identically distributed data scenarios. Besides, the model implements a Fully Homomorphic Encryption (FHE) scheme to enhance robustness against privacy leaks or attacks, enabling clients to offload computational work to the coordinator, which operates entirely on encrypted data. We achieve an efficient and secure distributed model with an improved representation capacity for this type of architecture. The source code used in the study is made publicly available.