Federated Learning Aggregation

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26 papers in the last four weeks, up 117% on the four weeks before. 0.3% of all new papers.

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Mechanistic Evidence for Preserved-but-Misaligned Representations in Non-IID FedAvg
Dec 28, 2025Muhammad Haseeb, Salaar Masood, Muhammad Abdullah Sohail +2Federated Learning Aggregation Non-IID Federated Learning
Department of Computer Science, Lahore University of Management Sciences, Lahore, Pakistan.
FedVideoMAE: Efficient Federated Video Moderation with Differential Privacy and Secure Aggregation
Dec 21, 2025Ziyuan Tao, Chuanzhi Xu, Sandaru Jayawardana +4Federated Learning Aggregation Video Content Moderation
School of Computing, Macquarie University, Sydney, Australia · School of Computer Science, The University of Sydney, Sydney, Australia · School of Electrical and Computer Engineering, The University of Sydney, Sydney, Australia
Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
Dec 3, 2025Seyed Mohammad Azimi-Abarghouyi, Carlo Fischione, Kaibin HuangFederated Learning Aggregation Communication-Efficient Distributed Training
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
A Nesterov-Accelerated Byzantine-Robust Federated Learning
Nov 4, 2025Lihan Xu, Xiaoyi Fan, Gang Wang +3Federated Learning Aggregation Byzantine-Robust Federated Learning
School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China · Artificial Intelligence Research Institute and Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing, Shenzhen MSU-BIT University, Shenzhen 518172, China · State Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China
FedIA: Importance-Aware Aggregation for Domain-Robust Federated Graph Learning
Sep 17, 2025Zhanting Zhou, Zeyu Ma, Ziqiang Zheng +1Federated Learning Aggregation Domain Generalization
University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China
FedDAF: Federated Domain Adaptation Using Model Functional Distance
Sep 15, 2025Mrinmay Sen, Sidhant Nair, C Krishna MohanFederated Learning Aggregation Domain Adaptation
Department of Artificial Intelligence, Indian Institute of Technology Hyderabad, Hyderabad, 502285, Telangana, India. · Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, Delhi, India.
FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing
Aug 16, 2025You Hak Lee, Keming Fan, Xiaofan Yu +4Compute-in-Memory Federated Learning Aggregation
University of California San Diego, USA
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
May 28, 2025Santhosh Parampottupadam, Melih Coşğun, Sarthak Pati +7Federated Learning Aggregation Differentially Private Stochastic Gradient Descent
German Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany · Department of Computer Engineering, Bilkent University, Universiteler 06800 Çankaya/Ankara, Türkiye · Medical Research Group, MLCommons, San Francisco, CA, USA +2
Federated Independent Component Analysis via Spectral Alignment and Robust Aggregation
May 26, 2025Xin Bing, Dian Jin, Yuqian ZhangFederated Learning Aggregation Independent Component Analysis
University of Toronto · National University of Singapore · Rutgers University
Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces
Mar 19, 2025Can Peng, Qianhui Men, Pramit Saha +5Federated Learning Aggregation Federated Continual Learning
University of Oxford, Oxford, United Kingdom · University of Bristol, Bristol, United Kingdom
Approaching the Harm of Gradient Attacks While Only Flipping Labels
Feb 28, 2025Abdessamad El-Kabid, El-Mahdi El-MhamdiFederated Learning Aggregation Adversarial Attacks
École Polytechnique
Influence-Oriented Personalized Federated Learning
Oct 4, 2024Yue Tan, Guodong Long, Jing Jiang +1Federated Learning Aggregation Non-IID Federated Learning
Griffith University · University of Technology Sydney · Hong Kong Polytechnic University
Byzantine-Robust Aggregation for Securing Decentralized Federated Learning
Sep 26, 2024Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo +1Federated Learning Aggregation Byzantine-Robust Federated Learning
atlanTTic Research Center – ICLAB – Universidade de Vigo, Vigo, 36310, Spain
FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge
Feb 26, 2024Tianhang Zheng, Yanlu Li, Bohan Deng +1Federated Learning Aggregation Byzantine-Robust Federated Learning
University of Missouri-Kansas City · University of Toronto
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering
Jun 22, 2023Tianyu Zhao, Junping Du, Yingxia Shao +1Federated Learning Aggregation Clustering
Beijing Key Laboratory of Intelligent Communication Software and Multimedia, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China
Class-wise Contribution Estimation via Logit Maximization for Federated Learning
Date pendingAsim Ukaye, Nurbek Tastan, Mubarak Abdu-Aguye +1Class-Imbalanced Learning Federated Learning Aggregation
Mohamed bin Zayed University of Artificial Intelligence, UAE · Michigan State University, USA
Byzantine-Robust Federated Fire Detection with a Rotating Coordinator
Date pendingGeorgia Argyrou, Aymen Bahroun, Hedi Fendri +1Federated Learning Aggregation Byzantine-Robust Federated Learning
Department of Computer Science, Aalto University, Espoo, Finland · Kudelski Labs

Dec 28, 2025, cs.LGJ/K move · Enter open · S save

Mechanistic Evidence for Preserved-but-Misaligned Representations in Non-IID FedAvg

Muhammad Haseeb, Salaar Masood, Muhammad Abdullah Sohail, Mohammad Fatim Shoaib+1

Department of Computer Science, Lahore University of Management Sciences, Lahore, Pakistan.

Federated Learning Aggregation Non-IID Federated Learning Federated Learning

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Federated Averaging (FedAvg) often degrades under non-IID client data, but it remains unclear whether this degradation reflects the loss of client-learned representations or a failure to use representations that are still present. We study this question mechanistically in sparse client-trained vision models, using dense-model controls to test whether the observed effects depend on sparsity. Our analysis combines class-specific circuit discovery, linear probing of frozen representations, head-only finetuning, and sparse feature dictionaries. Across CNN and ResNet models on CIFAR-10 and Fashion-MNIST, severe label skew can drive some per-class accuracies near zero even when class-specific internal structure remains recoverable. Linear probes substantially outperform the aggregated classifier, head-only finetuning partially restores accuracy, and USAE transfer reveals a largely shared feature basis between IID and non-IID models. Together, these diagnostics suggest that, in our setting, non-IID FedAvg degradation is not fully explained by representational erasure; it also reflects misalignment between preserved internal structure and the final prediction pathway.

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