arXiv Machine Learning

Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling

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 Machine Learning

Aug 4

Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization

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.

By Furkan Bagci, Busra Tegin, Mohammad Kazemi, Tolga M. Duman

safety

arXiv Machine Learning

Jun 9

Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design

arXiv:2410. 05662v4 Announce Type: replace Abstract: Most federated learning (FL) approaches assume a fixed device set.

By Zhan-Lun Chang, Dong-Jun Han, Seyyedali Hosseinalipour, Mung Chiang, Christopher G. Brinton

arXiv Machine Learning

Jun 24

Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation

arXiv:2606. 24340v1 Announce Type: new Abstract: In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures.

By Samer Nasser, Henrique Duarte Moura, Ritesh Kumar Singh, Maarten Weyn, Jeroen Famaey

agents reinforcement-learning fine-tuning efficiency

arXiv AI

Jul 15

Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination

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.

By Usman Haider, Karl Mason

reinforcement-learning benchmarks safety

Hugging Face Trending Papers

Jun 23

Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation

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.

agents reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Jun 16

Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data

arXiv:2601. 09304v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data.

By Sota Sugawara, Yuji Kawamata, Akihiro Toyoda, Tomoru Nakayama, Yukihiko Okada

arXiv Machine Learning

Jun 10

Toward Proactive RF Charging Scheduling: Generative AI for Decision Support

arXiv:2606. 10600v1 Announce Type: cross Abstract: Radio frequency wireless power transfer (RF-WPT) is an enabling technology for supporting uninterrupted communications in future Internet of Things systems by reducing the need for battery replacement and mitigating battery-waste-related issues.

By Amirhossein Azarbahram, Osmel M. Rosabal, David Ernesto Ruiz-Guirola, Melike Erol-Kantarci, Kaibin Huang, Onel L. A. L\'opez

arXiv Machine Learning

Jun 25

Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series

arXiv:2606. 24955v1 Announce Type: new Abstract: Power forecasting models deployed in real-world energy markets must operate under nonstationary conditions, where data distributions continually evolve due to weather variability, infrastructure upgrades, and changing consumption behaviors.

By Yujiang He, Frederic Uhrweiller, Bernhard Sick

arXiv Machine Learning

Aug 5

Design-Time Optimization of Deep Neural Networks for Intermittent Learning on Microcontrollers

arXiv:2608. 03589v1 Announce Type: new Abstract: We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs).

By Jakob Schubert, Maximilian Kasper, Maximilian Linke, Benedict Herzog, Mark Deutel, Axel Plinge, Dominik Seuss, Christopher Mutschler

agents

arXiv Machine Learning

Aug 10

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization

arXiv:2608. 06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications.

By Grigory Malinovsky

fine-tuning

arXiv Machine Learning

Jul 21

Bridging battery design and health assessment through virtual sensing and physics-informed learning

arXiv:2607. 16864v1 Announce Type: new Abstract: Supercharging of lithium-ion batteries (LiBs) requires robust health monitoring to ensure durability, safety, and user confidence, particularly for emerging vehicle-to-grid applications with bidirectional energy flows.

By Wendi Guo, S{\o}ren Byg Vilsen, Daniel Ioan Stroe, Yaqi Li, Yicun Huang, Ashima Verma, Daniel Brandell

rag diffusion benchmarks safety

arXiv AI

Jul 13

On-Device Adaptive Battery Power Prediction for Electric Vehicles

arXiv:2607. 09400v1 Announce Type: cross Abstract: Adaptive power management in Electric Vehicles (EVs) requires accurate power prediction.

By Avik Bhatnagar, Anton Paule, Tobias Schuermann, Sebastian Reiter, Oliver Bringmann

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