TL;DR
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.
EcoFL (Energy-Conscious Federated Learning), a modular, energy-aware benchmarking and orchestration framework for systematic evaluation of lightweight machine learning models under emulated edge hardware constraints, is presented.
Edge computing enables distributed intelligence in resource-constrained IoT environments. However, traditional Federated Learning (FL) struggles with heterogeneous device capabilities, dynamic network conditions, and non-IID data distributions, resulting in straggler effects, slow convergence, and inefficient resource...
The experimental results demonstrate that the proposed Trust-FedAvg framework substantially improves robustness over conventional FedAvg and remains competitive with established robust aggregation strategies, particularly under directional model-manipulation attacks and intermittent-connectivity conditions.
Results demonstrate the practicality of integrating predictive intelligence and decentralized coordination for scalable and secure FL, and avoids overloading any single node and ensures fault-tolerant, adaptive selection through continuous monitoring and helper-assisted data gathering.
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.
The proposed framework is validated by conducting simulation-based experiments on the benchmark datasets and synthetic autonomous workloads, where the novelty lies in the design of the system-level federated learning architecture, instead of the datasets themselves.
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