FLAT: An Efficient Federated Learning Scheme With Adaptive Topology and Secure Aggregation

Traditional federated learning requires each agent to send its local model to a central server for aggregation. However, directly transmitting the models to the server can raise privacy concerns, as the server may be able to reconstruct the original training data from the gradients of the agent models. To address this privacy issue, researchers have adopted secure aggregation methods for aggregating the global model. However, current secure aggregation schemes primarily use centralized aggregation concepts and masking techniques, which limit the efficiency of federated learning and hinder the further development of secure aggregation. In this paper, we leverage the concept of decentralization to reduce communication and computation overhead at individual nodes in secure aggregation, thereby improving the efficiency of federated learning. Additionally, we design a FedAvg aggregation algorithm based on secure multi-party computation technology and propose a dropout handling mechanism that differs from traditional secure aggregation methods. Through theoretical analysis, we show that the maximum communication complexity at individual nodes is reduced fromO(pn)to$O(p √ n)$. Finally, we conduct experiments to evaluate the proposed scheme and compare it with several baselines. The experimental results show that, in a local area network (LAN) environment, the execution speed of our scheme improves by 2.7×-4.3×, and in a wide area network (WAN) environment, the execution speed improves by 3.5×-3.85×.