TL;DR
A new hybrid decomposition approach for optimizing multi-model federated learning (MMFL) in edge computing environments that enhances the speed of convergence, contention of resource, and real-time performance, which makes it especially appropriate to apply it to the real-world, e.g., smart healthcare, autonomous vehicles, and IoT systems.
A novel algorithm named Group Relative Policy Optimization Based on Hierarchical Mean-Field Theory (OGRPO-HMF) is proposed, which can jointly optimize the local training of nodes and the global model aggregation of servers to comprehensively enhance the efficiency and performance of FEL.
A structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025 reveals that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations.
Simulation results validate the holistic integration with federated multi-agent learning, with emphasis on contention awareness and energy balancing is essential for scalable and efficient routing in next-generation dense FANETs.
Over-the-air FL with EH MDs under heterogeneous data distributions under heterogeneous data distributions is studied, and the proposed unified framework improves fairness or personalization, depending on the operating mode, while reducing communication overhead.
Semi-Asynchronous Federated Learning (SAFL) takes advantage of both synchronous and asynchronous FLs to train models. However, existing works in semi-asynchronous FLs fail to fully account for heterogeneities in both data and devices. To address these issues, we first propose a Clustered SAFL (CSAFL) framework and theoretically analyze its convergence loss. Then, a convergence loss minimization problem is formulated under the considerations of heterogeneities in device resources, fairness of cluster selection, and data heterogeneity. To address this complex problem due to nonlinearities and multi-dimensional decision variables, we first design a device clustering algorithm based on both devices’ model parameter differences and gradient directions between local and global models. Then, the original loss minimization problem is transformed into inter- and intra-cluster selection problems. For the inter-cluster selection problem, to ensure fairness, we propose a reinforcement learning-driven Lyapunov approach to fairly select clusters, where reinforcement learning (RL) is used to supervise the cluster selection results by the Lyapunov method. For the intra-cluster selection, we convert it into a constrained multi-armed bandit (MAB) problem in order to let devices within a cluster submit models synchronously. Then, a two-stage Upper Confidence Bound (UCB) scheme is proposed to obtain device selection results. Extensive numerical results with baselines show that our approach achieves up to 25% higher accuracy.
The Federated Green Anaconda Optimizer (FedGAO), an innovative FL framework inspired by the behavioral patterns of the Green Anaconda Optimizer (GAO), is proposed, demonstrating superior performance in terms of accuracy, convergence speed, and resource efficiency.
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