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HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning
May 30, 2026Amir Hossein Shahdadian, Ahmed M. Abdelmoniem, Mahdi Taheri +2Non-IID Federated Learning Federated Learning
University of Naples "Federico II", Italy · Queen Mary University of London, United Kingdom · Brandenburg University of Technology Cottbus-Senftenberg, Germany +2
Canonicalized Stable-List Replay for Private Federated Continual Learning over Language-Model Embeddings
May 29, 2026Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj SharmaFederated Continual Learning Replay-Based Continual Learning
Department of Computer Science, Iowa State University · Department of Computer Science & Engineering, BRAC University · Department of Civil, Construction & Environmental Engineering, Iowa State University
Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks
May 29, 2026Kangmin Kim, Jaeyoung SongEnergy-Efficient ML Internet of Things
School of Electrical and Electronics Engineering, Pusan National University, Busan, 46241, Korea
Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
May 29, 2026Jabin Koo, Hoyoung Kim, Minwoo Jang +1LLM Alignment Preference Alignment
Department of CSE, POSTECH, Pohang, Republic of Korea · National AI Research Lab, Seoul, Republic of Korea · Graduate School of AI, POSTECH, Pohang, Republic of Korea
XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT
May 28, 2026Ambreen Aslam, Maaz Hassan, Bibi Zahra +1Federated Learning Aggregation Network Intrusion Detection
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan
Fairness-Aware Federated Learning with Trajectory Shapley Value
May 28, 2026Daniel Kuznetsov, Ziqi WangFederated Learning Aggregation Algorithmic Fairness
Faculty of Mathematics, Ecole Normale Supérieure Paris-Saclay, 4 Av. des Sciences, 91190 Gif-sur-Yvette, France. · Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstrasse 11, 91058 Erlangen, Germany.
Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channels
May 28, 2026Anthony Ayli, Khalil Harris, Jihad Fahs +1Federated Learning Aggregation Homomorphic Encryption
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
May 28, 2026Hoang M. Ngo, Quan Nguyen, Wanli Xing +1Federated Learning Aggregation Quantum Machine Learning
Department of Computer & Information Science & Engineering University of Florida · Frost Institute for Data Science and Computing University of Miami
Mitigating Stethoscope-Induced Shortcuts in Respiratory Sound Classification under Federated Domain Generalization with Causality-Inspired Interventions
May 28, 2026Heejoon Koo, Yoon Tae Kim, Miika Toikkanen +1OOD Generalization Shortcut Learning
University of Illinois Urbana-Champaign, USA · RSC LAB, MODULABS, Korea · Wonkwang University, Korea
FPLIER: Federated Pathway-Level Information Extractor
May 28, 2026Daniele Malpetti, Christian Berchtold, Francesco Gualdi +3Membership Inference Attacks Privacy-Preserving ML
1Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI, Lugano, Switzerland · 2Swiss Institute of Bioinformatics (SIB), Lugano, Switzerland
FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation
May 28, 2026Zehao Wang, Guanglei Yang, Yihan Zeng +4Low-Rank Adaptation Federated Fine-Tuning
1Harbin Institute of Technology · 2Huawei Noah’s Ark Lab · University College Dublin
Anytime-Valid Federated Conformal RAG for LLM Swarms
May 27, 2026Prasanjit Dubey, Xiaoming HuoRetrieval-Augmented Generation Anytime-Valid Inference
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A.
FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
May 27, 2026Yuchen Hou, Yongshan Chen, Zhuowen Zou +4RL Control Federated Learning Aggregation
Northeastern University · University of California, Irvine · The George Washington University
FedMPT: Federated Multi-label Prompt Tuning of Vision-Language Models
May 27, 2026Xucong Wang, Pengkun Wang, Zhe Zhao +3Multi-Label Classification VLM Adaptation
University of Science and Technology of China (USTC) · Suzhou Institute for Advanced Research, USTC · City University of Hong Kong
Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning
May 27, 2026Yuting Ma, Lechao Cheng, Xiaohua XuVLM Adaptation Vision-Language Alignment
School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China · School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China.
Pattern Recognition Tasks with Personalized Federated Learning
May 27, 2026Md. Arifur Rahman, Isha Das, Mushfiqur Rahman Abir +4Image Classification Federated Learning
College of Graduate and Professional Studies, Trine University, Angola, IN 46703, United States. · Network Communication and IoT Lab, Chittagong University of Engineering and Technology, Chittagong, Bangladesh. · Department of Computer Science and Engineering, American International University-Bangladesh, Dhaka, Bangladesh. +4
A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices
May 26, 2026Chaimae Jallouli, Karim Boubouh, Robert BasmadjianEnergy-Efficient ML Heterogeneous Computing
Mohammed VI Polytechnic University, Benguerir, Morocco · Khalifa University, Abu Dhabi, UAE
Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation
May 26, 2026Khayyam Nosrati, Martin Uray, Saverio Messineo +2Industrial Anomaly Detection Time-Series Anomaly Detection
Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences, Austria · Department of Artificial Intelligence and Human Interfaces, Paris Lodron University of Salzburg, Austria
HEAL: Resilient and Self-* Hub-based Learning
May 26, 2026Mohamed Amine Legheraba, Stefan Galkiewicz, Maria Gradinariu Potop-Butucaru +1Decentralized Learning Federated Learning
NPA · Sorbonne University, CNRS, LIP6, F-75005 Paris, France · NPA, IUF, LINCS +1
Image Feature Fusion-based Federated Client Unlearning (FCU)
May 26, 2026Hangyi Shen, Yizhi Pan, Tiansuo Li +2Federated Unlearning Mixup
11Hangzhou Medical College, Hangzhou, China
Separate Aggregation of Split Network for Personalized Federated Learning
May 26, 2026Yunseok Kang, Jaeyoung SongFederated Learning Aggregation Split Federated Learning
Department of Electronics Engineering, Pusan National University, Republic of Korea
Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
May 25, 2026Zhishuai Guo, Wenhan Wu, Chen Chen +3Graph Neural Networks Communication-Efficient Distributed Training
Department of Computer Science Northern Illinois University · Department of Computer Science University of North Carolina at Charlotte · Department of Computer Science University of Central Florida +2
Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning
May 25, 2026Zhen Li, Jun Cai, Chao Yang +1Multi-Objective Reinforcement Learning Edge Inference
Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada · School of Automation, Guangdong University of Technology, China
Personalized Federated Learning by Energy-Efficient UAV Communications
May 24, 2026Shiqian Guo, Jianqing Liu, Beatriz LorenzoEnergy-Efficient ML Client Selection in Federated Learning
Department of Computer Science, North Carolina State University, Raleigh, NC 27606 USA · University of Massachusetts, Amherst, MA 01003 USA
On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach
May 24, 2026Jiahui Bai, Hai Dong, A. K. QinMulti-Agent Coordination Federated Learning Aggregation
School of Computer Technologies, RMIT University Melbourne, VIC, Australia · School of Science, Computing and Engineering Technologies, Swinburne University of Technology Hawthorn, VIC, Australia
Hardware-Aware Federated Learning for Speech Emotion Recognition
May 23, 2026Beyazit Bestami Yuksel, Emrah DikbiyikCommunication-Efficient Distributed Training Client Selection in Federated Learning
Computer Engineering Istanbul Technical University Istanbul, Turkey · Department of Computer Technologies Istanbul University-Cerrahpaşa Istanbul, Turkey
Rethinking Federated Unlearning via the Lens of Memorization
May 23, 2026Jiaheng Wei, Yanjun Zhang, He Zhang +5Federated Unlearning Neural Network Memorization
Royal Melbourne Institute of Technology · Griffith University · Swinburne University of Technology
ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning
May 23, 2026Karan Sharma, Aditya Tripathi, Rahul Mishra +1Federated Learning Aggregation Federated Learning
DA-IICT Gandhinagar, India · IIT Patna Patna, India
PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets
May 22, 2026Anisa Halimi, Liubov Nedoshivina, Kieran Fraser +1Healthcare Privacy-Preserving ML
IBM Research Dublin, Ireland
FD-RAG: Federated Dual-System Retrieval-Augmented Generation
May 22, 2026Tianhao Gao, Kai Yang, Yiyang LiRetrieval-Augmented Generation Memory-Augmented Language Models
School of Computer Science and Technology, Tongji University

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HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning

Amir Hossein Shahdadian, Ahmed M. Abdelmoniem, Mahdi Taheri, Samira Nazari+1

University of Naples "Federico II", Italy · Queen Mary University of London, United Kingdom · Brandenburg University of Technology Cottbus-Senftenberg, Germany+2

Non-IID Federated Learning Federated Learning Heterogeneous Federated Learning

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Edge services increasingly use federated learning to personalize on-device models while keeping sensitive data local. In practice, deployments must handle heterogeneity in both client resources and local data distributions. Model-heterogeneous federated learning lowers client cost by allowing each client to train a subnet of a shared supernet, but most subnet-allocation policies are driven by device constraints and do not explicitly account for statistical heterogeneity. This paper proposes Heterogeneity-Aware Subnet Allocation (HASA), a train-only rule that assigns subnet widths based on client heterogeneity scores computed from local training data while enforcing a fixed size-weighted compute budget. This design enables budget-matched comparisons with alternative allocation policies. On an article-title next-word prediction benchmark with seven clients, HASA improves unweighted mean client test accuracy over uniform allocation across 10 matched seeds, increasing mean client test accuracy from 13.82 percent to 14.32 percent, and improves worst-client accuracy on average. In a matched-budget comparison with representative partial-training baselines, HASA achieves the strongest worst-client and tail-client accuracy on this benchmark. A directionality ablation shows that assigning smaller subnets to more heterogeneous clients degrades both mean and tail performance. A cross-domain image-classification study further shows that the effectiveness of heterogeneity-aware allocation depends on how well the heterogeneity score reflects clients' need for additional model width.

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