Federated sequential recommendation (FedSeqRec) aims to perform next-item prediction while keeping user data decentralised, yet model quality is frequently constrained by fragmented, noisy, and homogeneous interaction logs stored on individual devices. Many existing approaches attempt to compensate through manual data augmentation or additional server-side constraints, but these strategies either introduce limited semantic diversity or increase system overhead. To overcome these challenges, we propose \textbf{LUMOS}, a parameter-isolated FedSeqRec architecture that integrates large language models (LLMs) as \emph{local semantic generators}. Instead of sharing gradients or auxiliary parameters, LUMOS privately invokes an on-device LLM to construct three complementary sequence variants from each user history: (i) \emph{future-oriented} trajectories that infer plausible behavioural continuations, (ii) \emph{semantically equivalent rephrasings} that retain user intent while diversifying interaction patterns, and (iii) \emph{preference-inconsistent counterfactuals} that serve as informative negatives. These synthesized sequences are jointly encoded within the federated backbone through a tri-view contrastive optimisation scheme, enabling richer representation learning without exposing sensitive information. Experimental results across three public benchmarks show that LUMOS achieves consistent gains over competitive centralised and federated baselines on HR@20 and NDCG@20. In addition, the use of semantically grounded positive signals and counterfactual negatives improves robustness under noisy and adversarial environments, even without dedicated server-side protection modules. Overall, this work demonstrates the potential of LLM-driven semantic generation as a new paradigm for advancing privacy-preserving federated recommendation.
Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments
Engineering Product Development Singapore University of Technology and Design（新加坡科技与设计大学工程产品开发学院）；School of Automation Chongqing University（重庆大学自动化学院）；Institute for Infocomm Research Agency for Science, Technology and Research（信息与通信研究所）；School of Electrical and Electronics Engineering Nanyang Technological University（南洋理工大学电子与电气工程学院）
We present federated momentum contrastive clustering (FedMCC), a learning framework that can not only extract discriminative representations over distributed local data but also perform data clustering. In FedMCC, a transformed data pair passes through both the online and target networks, resulting in four representations over which the losses are determined. The resulting high-quality representations generated by FedMCC can outperform several existing self-supervised learning methods for linear evaluation and semi-supervised learning tasks. FedMCC can easily be adapted to ordinary centralized…
Australian Artificial Intelligence Institute（澳大利亚人工智能研究所）；University of Technology Sydney（悉尼科技大学）；Google Research（谷歌研究院）；University of Washington（华盛顿大学）；University of Maryland（马里兰大学）
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to learn collaboratively without sharing their private data. However, excessive computation and communication demands pose challenges to current FL frameworks, especially when training large-scale models. To prevent these issues from hindering the deployment of FL systems, we propose a lightweight framework where clients jointly learn to fuse the representations generated by multiple fixed pre-trained models rather than training a large-scale model from scratch. This leads us to a more practical FL problem…
Department of Computer Science, University of Oxford（牛津大学计算机科学系）；University of Oxford（牛津大学）；Oxford, UK（英国牛津（这里仅保留城市和国家，实际机构为牛津大学））
A label-efficient paradigm in computer vision is based on self-supervised contrastive pre-training on unlabeled data followed by fine-tuning with a small number of labels. Making practical use of a federated computing environment in the clinical domain and learning on medical images poses specific challenges. In this work, we propose FedMoCo, a robust federated contrastive learning (FCL) framework, which makes efficient use of decentralized unlabeled medical data. FedMoCo has two novel modules: metadata transfer, an inter-node statistical data augmentation module, and self-adaptive aggregation…
Tsinghua University（清华大学）；Microsoft Research Asia（微软亚洲研究院）
Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus on centralized data, where abundant and high-quality negative samples are easy to obtain. However, centralized user data storage and exploitation may lead to privacy risks and concerns, while decentralized user data on a single client can be too sparse and biased for accurate contrastive learning. In this paper, we propose a federated contrastive learning method named FedCL for privacy-preserving recommendation, which…
Multimodal Federated Learning via Contrastive Representation Ensemble
Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Waterloo（滑铁卢大学）；Carnegie Mellon University（卡内基梅隆大学）
Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clients
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, College of Computer Science and Software Engineering, Hohai University, Nanjing, China（水利部水大数据技术重点实验室，计算机科学与软件工程学院，河海大学，南京，中国）；Department of Computer Science, City University of Hong Kong, Hong Kong, China（计算机科学系，香港城市大学，香港，中国）；State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China（新型软件技术国家重点实验室，南京大学，南京，中国）；School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China（人工智能与信息技术学院，南京中医药大学，南京，中国）
University of Pittsburgh（匹兹堡大学）；University of Notre Dame（圣母大学）；George Mason University（乔治梅森大学）；University of New Mexico（新墨西哥大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
Deep learning models have been deployed in an increasing number of edge and mobile devices to provide healthcare. These models rely on training with a tremendous amount of labeled data to achieve high accuracy. However, for medical applications such as dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients, and each device only has a limited amount of data. Directly learning from limited data greatly deteriorates the performance of learned models. Federated learning (FL) can train models by using data distri…
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning
Northeastern University（东北大学）；Illinois Institute of Technology（伊利诺伊理工学院）；Rochester Institute of Technology（罗切斯特理工学院）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）；University of Southern California（南加利福尼亚大学）；University of Michigan（密歇根大学）
Federated Contrastive Learning for Personalized Semantic Communication
Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）；University of Essex（埃塞克斯大学）；Peng Cheng Laboratory（鹏城实验室）；Queen Mary University of London（伦敦玛丽女王大学）
University of Pittsburgh（匹兹堡大学）；University of Notre Dame（圣母大学）
Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) can help in this regard by learning a shared model while keeping training data local for privacy. Traditional FL requires fully-labeled data for training, which is inconvenient or sometimes infeasible to obtain due to high labeling cost and the requirement of expertise. Contrastive learning (CL), as a self-supervised learning approach, can effectivel…
Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；Institute of Artificial Intelligence Research, Jiangsu Second Normal University（江苏第二师范学院人工智能研究所）；Digital Research Institute, ENN Group（ENN集团数字研究院）；School of Computing, Engineering and Mathematical Sciences, La Trobe University（拉筹伯大学计算、工程与数学科学学院）；College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）
University of Pittsburgh（匹兹堡大学）；University of Notre Dame（圣母大学）；George Mason University（乔治梅森大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
In dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients. Federated learning (FL) can use decentralized data to train models while keeping data local. Existing FL methods assume all the data have labels. However, medical data often comes without full labels due to high labeling costs. Self-supervised learning (SSL) methods, contrastive learning (CL) and masked autoencoders (MAE), can leverage the unlabeled data to pre-train models, followed by fine-tuning with limited labels. However, combining SSL and FL h…
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data
University of California, San Francisco（加州大学旧金山分校）；Lehigh University（利哈伊大学）；New Jersey Institute of Technology（新泽西理工学院）；University of Louisiana at Lafayette（路易斯安那大学拉法叶分校）
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
Shanghai Jiao Tong University（上海交通大学）；Institute for AI Industry Research, Tsinghua University（清华大学智能产业研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Queen’s University Belfast（英国女王大学）
GTFLAT, as a game theory-based add-on, addresses an important research question: How can a federated learning algorithm achieve better performance and training efficiency by setting more effective adaptive weights for averaging in the model aggregation phase? The main objectives for the ideal method of answering the question are: (1) empowering federated learning algorithms to reach better performance in fewer communication rounds, notably in the face of heterogeneous scenarios, and last but not least, (2) being easy to use alongside the state-of-the-art federated learning algorithms as a new …
University of Pittsburgh（匹兹堡大学）；University of Notre Dame（圣母大学）；George Mason University（乔治梅森大学）
Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) can learn a shared model from decentralized data. But traditional FL requires fully-labeled data for training, which is very expensive to obtain. Self-supervised contrastive learning (CL) can learn from unlabeled data for pre-training, followed by fine-tuning with limited annotations. However, when adopting CL in FL, the limited data diversity on eac…
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Public Affairs, Zhejiang University（浙江大学公共管理学院）；Tongdun Technology（同盾科技）
To leverage enormous unlabeled data on distributed edge devices, we formulate a new problem in federated learning called Federated Unsupervised Representation Learning (FURL) to learn a common representation model without supervision while preserving data privacy. FURL poses two new challenges: (1) data distribution shift (Non-IID distribution) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces. (2) without the unified information among clients in FURL, the representations across clients would be misaligned. To address the…
Tencent AI Lab（腾讯AI实验室）；Tencent Data Platform（腾讯数据平台）
MoCo is effective for unsupervised image representation learning. In this paper, we propose VideoMoCo for unsupervised video representation learning. Given a video sequence as an input sample, we improve the temporal feature representations of MoCo from two perspectives. First, we introduce a generator to drop out several frames from this sample temporally. The discriminator is then learned to encode similar feature representations regardless of frame removals. By adaptively dropping out different frames during training iterations of adversarial learning, we augment this input sample to train …
BadPromptFL: A Novel Backdoor Threat to Prompt-based Federated Learning in Multimodal Models
School of Information and Communication Engineering, Dalian University of Technology（信息与通信工程学院，大连理工大学）；School of Computer Science and Technology, Anhui University（计算机科学与技术学院，安徽大学）；New Laboratory of Pattern Recognition (NLPR) State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation, Chinese Academy of Sciences (CASIA)（模式识别新实验室（NLPR）多模态人工智能系统国家重点实验室（MAIS）自动化研究所，中国科学院（CASIA））
A deep cut into Split Federated Self-supervised Learning
Jagiellonian University（雅盖隆大学）；Faculty of Mathematics and Computer Science（数学与计算机科学学院）；Doctoral School of Exact and Natural Sciences（精确与自然科学博士学院）；IDEAS NCBR
OPPO Research Institute（OPPO研究院）；Zhejiang University（浙江大学）
This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection. Existing works either conduct annotation-guided local training which requires frequent communication or aggregates the client models via weight averaging which has potential risks of privacy exposure. To tackle the above problems, we first identify that self-supervised contrastive local training is robust against the non-identically distributed data, which provides the feasibility of longer local training and thus reduces the communication cost. Then …
A Contrastive Pre-trained Foundation Model for Deciphering Imaging Noisomics across Modalities
College of Biomedical Engineering, Yiwu Research Institute, Fudan University, Shanghai, China（复旦大学生物医学工程学院）；Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China（清华大学心理学与认知科学系）；Shanghai Innovation Institute, Shanghai, China（上海创新研究院）；DAMO Academy, Alibaba Group, Beijing, China（阿里云达摩院）；Hupan Laboratory, Hangzhou, Zhejiang, China（华平实验室）
National University of Singapore（新加坡国立大学）；UC Berkeley（加州大学伯克利分校）
Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models. In this paper, we propose MOON: model-contrastive federated learning. MOON is a simple and effective federated learning framework. The key idea of MOON is to utilize the similarity between m…
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongguancun Laboratory（中关村实验室）；Beijing Jiaotong University（北京交通大学）
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Multi-granularity Contrastive Cross-modal Collaborative Generation for End-to-End Long-term Video Question Answering
School of Information Science and Technology, Hangzhou Normal University（杭州师范大学信息科学与技术学院）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；School of Computer Science and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机科学与技术学院）；Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术系）
FedReplay: A Feature Replay Assisted Federated Transfer Learning Framework for Efficient and Privacy-Preserving Smart Agriculture
Department of Electrical and Computer Engineering, The University of Alabama（电气与计算机工程系，阿拉巴马大学）；Electrical and Computer Engineering, Michigan State University（电气与计算机工程，密歇根州立大学）；Agricultural and Biological Engineering, Mississippi State University（农业与生物工程，密苏里州立大学）；Department of Computer Science, University of Alabama（计算机科学系，阿拉巴马大学）；USDA-ARS Genetics and Sustainbale Agriculture（美国农业部ARS基因与可持续农业）
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；Sun Yat-sen University（中山大学）；Chongqing University（重庆大学）；Shanghai Pudong Development Bank（上海浦东发展银行）
