arXiv:2511. 11949v2 Announce Type: replace Abstract: Federated learning (FL) is a powerful paradigm for distributed learning, but increasing model complexity leads to significant energy consumption from client-side computations for local training.
arXiv:2608. 01426v1 Announce Type: new Abstract: Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability.
arXiv:2410. 05662v4 Announce Type: replace Abstract: Most federated learning (FL) approaches assume a fixed device set.
arXiv:2606. 24340v1 Announce Type: new Abstract: In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures.
DART-FL is a multitask federated learning framework designed for edge devices that must balance online inference and model training under limited resources. It dynamically allocates resources between inference and training based on current inference backlog and service capacity, then distributes remaining training capacity among tasks using a queue‑aware scheduler that adjusts loss weights. Experiments on image classification datasets with synthetic and real workloads show that DART‑FL adapts to bursty inference demand, improving accuracy for high‑demand tasks while preserving overall multitask performance.
arXiv:2607. 12763v1 Announce Type: cross Abstract: Federated Reinforcement Learning (FedRL) enables coordination of distributed energy resources without sharing raw local data, but standard aggregation methods such as FedAvg do not account for system-level constraints, often leading to unsafe global behavior.
In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy.
BIPPO (Budget-aware Independent Proximal Policy Optimization) is a multi‑agent reinforcement learning framework designed for energy‑efficient client selection in federated learning (FL) over IoT systems. It addresses infrastructure constraints such as limited resources and device churn, which traditional FL and RL approaches overlook. Evaluated on two image‑classification tasks with non‑IID data, BIPPO improves mean accuracy over non‑RL methods, standard PPO, and IPPO while consuming only a negligible portion of the budget, even as client numbers grow.
The paper introduces FedSWE, a federated learning algorithm designed to handle non‑stationary and heterogeneous client availability without requiring prior real‑time knowledge of which devices are online. FedSWE compensates for missed computations, stabilizes global updates, and mixes local updates through implicit gossiping, all while adding only modest memory and computational overhead. The authors prove that FedSWE converges to a stationary point for non‑convex objectives and achieves linear speedup in certain scenarios, and they validate these claims with experiments on real‑world datasets featuring diverse client unavailability patterns.
arXiv:2609.15389v1 Announce Type: cross Abstract: As artificial intelligence systems increasingly rely on distributed and collaborative training, the energy footprint of these processes becomes a sha...
arXiv:2601. 09304v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data.
FL-MAESTRO is a multi‑agent orchestrator that uses three specialized large language model agents to jointly decide the communication topology, per‑client resource allocation, and aggregation rule in each federated learning round. A coordinator merges the agents’ analyses, and a non‑LLM feasibility check validates the decision before execution. By filtering out clients whose updates would never be aggregated, the system eliminates the main source of wasted round energy in volatile edge networks and works across heterogeneous device classes without per‑class energy models, achieving comparable accuracy to the best energy‑aware baseline while reducing wasted energy from over a third to near zero on a non‑IID CIFAR‑10 benchmark.
SynthCharge is a parametric generator that creates diverse, feasibility‑screened instances of the electric vehicle routing problem with time windows (EVRPTW). It produces instances ranging from 5 to 100 customers (up to 500 in theory) with adaptive energy capacity scaling and range‑aware charging station placement, filtering out unsolvable cases via a fast feasibility screening process. This dynamic benchmarking infrastructure enables systematic evaluation of learning‑based routing and data‑driven approaches.