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 effic...
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 theo...
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
