arXiv Machine Learning June 10, 2026 By Chanuka A. S. Hewa Kaluannakkage, Rajkumar Buyya

Inverse Probability Weighting and Age-of-Information Aggregation for Decentralized Federated Learning under Partial Reception

Read the original on arXiv Machine Learning →

arXiv:2606. 10774v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) over lossy wireless networks faces two key challenges: selection bias, where updates from poor-quality links are systematically underrepresented due to partial model reception, and update staleness, where asynchronous nodes contribute outdated information.

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

arXiv Machine Learning

Jun 17

Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation

arXiv:2606. 10774v2 Announce Type: replace Abstract: Decentralized Federated Learning(DFL) enables collaborative model training across wireless edge nodes, including IoT deployments, autonomous vehicles, UAV swarms, and satellite constellations.

By Chanuka A. S. Hewa Kaluannakkage, Rajkumar Buyya

agents benchmarks safety

arXiv Machine Learning

Jul 7

Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks

arXiv:2607. 04218v1 Announce Type: new Abstract: The growing demand for privacy-preserving, data-intensive applications such as IoT, augmented reality, and autonomous systems positions Federated Learning (FL) as a key enabler in 6G networks.

By Zubaida Fatima, Zubair Shaban, Yusuf Jamal, Nazreen Shah, Ranjitha Prasad, B. N. Bharath

agents benchmarks safety

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 AI

Aug 10

FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks

arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.

By Majid Kundroo, Tinku Singh, Taehong Kim

safety

arXiv AI

1d ago

Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication

arXiv:2608. 15256v1 Announce Type: new Abstract: Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL).

By Lin Yin, Tiejun Lv, Weicai Li, Xi Yu, Xiaoyu He

safety

arXiv AI

Aug 11

FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning

arXiv:2608. 09208v1 Announce Type: cross Abstract: Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL).

By Van Truong Vo, Khoa Nguyen, Taehong Kim

Related stories

Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation

Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks

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

FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks

Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication

FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning