TUNE-FL: Adaptive Semi-Synchronous Semi-Decentralized Federated Learning

Today, Federated Learning (FL) stands out as the solution to addressing the challenges of distributed computing and empowering a wide range of edge devices with artificial intelligence capabilities. One variant of FL called semi-decentralized FL (SDFL) enables multiple server units to coordinate the learning task instead of relying on only one central server, hence preventing single-point failures. However, SDFL requires careful consideration regarding the coordination between server nodes, and dealing with the heterogeneous computing resources and data distributions across end devices (FL clients). Therefore, we propose TUNE-FL, an adapTive semi-synchronoUs semi-deceNtralizEd Federated Learning that addresses the clients' heterogeneity challenges. TUNE-FL alleviates these challenges by (i) ensuring consensus regardless of the network topology, and (ii) deploying an adaptive semi-synchronous mechanism for coordinating the learning process across all nodes while taking into consideration the heterogeneity presented by end devices. We evaluated TUNE-FL for the intrusion detection system (IDS) datasets and compared it with the three most representative baseline models. The experimental results demonstrate that TUNE-FL outperforms the baselines in accuracy while greatly reducing the duration of FL training by approximately 97 times.