arXiv Machine Learning August 4, 2026 By Furkan Bagci, Busra Tegin, Mohammad Kazemi, Tolga M. Duman

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

Jun 26

Quantization in Federated Learning: Methods, Challenges and Future Directions

arXiv:2606. 26822v1 Announce Type: new Abstract: Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally constrained by communication bottlenecks, device heterogeneity, and the challenges of training under statistically non-IID data.

By Farwa Ikram, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino

efficiency safety

arXiv Machine Learning

Jul 16

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.

By Eunjeong Jeong, Nikolaos Pappas

arXiv Machine Learning

Aug 3

GQ-FSL: Green Quantized Federated Split Learning

arXiv:2607. 29659v1 Announce Type: new Abstract: Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices.

By Idan Roth, Lutz Lampe

efficiency benchmarks

arXiv Machine Learning

Jul 21

Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).

By Ferdous Pervej, Minseok Choi, Andreas F. Molisch

computer-vision efficiency

arXiv Machine Learning

Jun 8

Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers

arXiv:2606. 06687v1 Announce Type: new Abstract: We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers.

By Su Wang, Mung Chiang, H. Vincent Poor

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

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