arXiv Machine Learning

Robust Federated Learning Under Real-World Client Churn

arXiv:2607. 06979v1 Announce Type: new Abstract: Federated Learning (FL) enables training shared models on private, on-device data, but production deployments remain constrained to slow, multi-day refresh cycles due to the complexity of coordinating massive client populations.

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

Jun 11

Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data

arXiv:2606. 11272v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity.

By Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange, Francesco Marcelloni

benchmarks safety

arXiv Machine Learning

6d ago

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.

By Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay

arXiv Machine Learning

Aug 6

Multi-Objective Ranking for Live-Streaming: Balancing Fresh and Delayed Signals with Segment-Aware Targeting

arXiv:2608. 04455v1 Announce Type: cross Abstract: One of the most challenging problems entertainment live-streaming services face in recommendation systems is that user behaviors are sparse and delayed, and interaction data exhibits bias for different user segments.

By Xiaoyi Gu, Julia Tavares, Eder Santana, Carlos Mendoza-Cardenas, Nikita Mishra, Saad Ali

safety

arXiv Machine Learning

Jul 21

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning

arXiv:2502. 08829v2 Announce Type: replace Abstract: Federated learning (FL) with non-IID data often degrades client performance below local training baselines.

By Ahmed Elhussein, Florent Pollet, Gamze G\"ursoy

efficiency

arXiv AI

Jul 28

AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt

arXiv:2512. 24625v3 Announce Type: replace-cross Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management.

By Zijian Zhao, Yitong Shang, Sen Li

safety

arXiv Machine Learning

Aug 5

When RL Meets Adaptive Speculative Training: A Unified Training-Serving System

arXiv:2602. 06932v5 Announce Type: replace Abstract: Speculative decoding can significantly accelerate LLM serving, yet most deployments today disentangle speculator training from serving, treating speculator training as a standalone offline modeling problem.

By Junxiong Wang, Fengxiang Bie, Jisen Li, Zhongzhu Zhou, Zelei Shao, Yubo Wang, Yinghui Liu, Qingyang Wu, Avner May, Sri Yanamandra, Ce Zhang, Tri Dao, Percy Liang, Ben Athiwaratkun, Shuaiwen Leon Song, Chenfeng Xu, Xiaoxia Wu

llms reinforcement-learning

arXiv Machine Learning

Jul 14

COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication

arXiv:2605. 11165v3 Announce Type: replace Abstract: Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution.

By Ben Rachmut, Luise Ge, William Yeoh, Ning Zhang, Yevgeniy Vorobeychik

efficiency benchmarks

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

1d ago

FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules

arXiv:2505. 04535v4 Announce Type: replace Abstract: Federated Learning (FL) enables the utilization of vast, previously inaccessible data sources.

By Michael Theologitis, Vasilis Samoladas, Antonios Deligiannakis

llms nlp fine-tuning

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

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