Federated Class-Incremental Learning (FCIL) is an underexplored yet pivotal issue, involving the dynamic addition of new classes in the context of federated learning. In this field, Data-Free Knowledge Transfer (DFKT) plays a crucial role in addressing catastrophic forgetting and data privacy problems. However, prior approaches lack the crucial synergy between DFKT and the model training phases, causing DFKT to encounter difficulties in generating high-quality data from a non-anchored latent space of the old task model. In this paper, we introduce LANDER (Label Text Centered Data-Free Knowledge Transfer) to address this issue by utilizing label text embeddings (LTE) produced by pretrained language models. Specifically, during the model training phase, our approach treats LTE as anchor points and constrains the feature embeddings of corresponding training samples around them, enriching the surrounding area with more meaningful information. In the DFKT phase, by using these LTE anchors, LANDER can synthesize more meaningful samples, thereby effectively addressing the forgetting problem. Additionally, instead of tightly constraining embeddings toward the anchor, the Bounding Loss is introduced to encourage sample embeddings to remain flexible within a defined radius. This approach preserves the natural differences in sample embeddings and mitigates the embedding overlap caused by heterogeneous federated settings. Extensive experiments conducted on CIFAR100, Tiny-ImageNet, and ImageNet demonstrate that LANDER significantly outperforms previous methods and achieves state-of-the-art performance in FCIL. The code is available at https://github.com/tmtuan1307/lander.
Federated Learning is a fast growing area of ML where the training datasets are extremely distributed, all while dynamically changing over time. Models need to be trained on clients' devices without any guarantees for either homogeneity or stationarity of the local private data. The need for continual training has also risen, due to the ever-increasing production of in-task data. However, pursuing both directions at the same time is challenging, since client data privacy is a major constraint, especially for rehearsal methods. Herein, we propose a novel algorithm to address the incremental lea…
Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator
College of Engineering & Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；Vietnam National University - Ho Chi Minh city, University of Science（越南国家大学胡志明市分校自然科学大学）；Technische Universität Berlin（柏林工业大学）
Institut LIST, CEA（法国原子能与替代能源委员会 LIST 研究所）；Université Paris-Saclay（巴黎萨克雷大学）
Federated learning enables different parties to collaboratively build a global model under the orchestration of a server while keeping the training data on clients' devices. However, performance is affected when clients have heterogeneous data. To cope with this problem, we assume that despite data heterogeneity, there are groups of clients who have similar data distributions that can be clustered. In previous approaches, in order to cluster clients the server requires clients to send their parameters simultaneously. However, this can be problematic in a context where there is a significant nu…
Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
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）
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Northwestern University（西北大学）；University of Technology Sydney（悉尼科技大学）
Federated learning (FL) has attracted growing attention via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume object classes of the overall framework are fixed over time. It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes. Moreover, new clients with unseen new classes may participate in the FL training, further aggravating the catastrophic forgetting o…
University of Padova（帕多瓦大学）；Uppsala University（乌普萨拉大学）
There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scenarios where the network is strongly heterogeneous in terms of communication resources (e.g., bandwidth) and data distribution. In these cases, communication between local machines (agents) and the central server (Master) is a main consideration. In this work, we present SHED, an original communication-constrained Newton-type (NT) algorithm designed to accelerate FL in such heterogeneous scenarios. SHED is by design ro…
Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a blessing). However, little research has considered both directions simultaneously. In this paper, we first investigate the relationship between them by analyzing Federated Averaging at the client level and determine that a better federated global model performance does not constantly improve personalization. To elucidate the cause of this personalization performance degradation problem, we decompose the entire network in…
Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuning
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（IHPC），科技研究局（A*STAR））；EVYD Technology（EVYD技术）
Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (non-iid), and Out of Distribution(OOD) generalization for the learned models can be poor. Besides this challenge…
Federated Learning is a distributed machine-learning environment that allows clients to learn collaboratively without sharing private data. This is accomplished by exchanging parameters. However, the differences in data distributions and computing resources among clients make related studies difficult. To address these heterogeneous problems, we propose a novel Federated Learning method. Our method utilizes a pre-trained model as the backbone of the local model, with fully connected layers comprising the head. The backbone extracts features for the head, and the embedding vector of classes is …
Inner Mongolia University of Technology（内蒙古工业大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；New Jersey Institute of Technology（新泽西理工学院）
Advanced researches on connected vehicles have recently targeted to the integration of vehicle-to-everything (V2X) networks with Machine Learning (ML) tools and distributed decision making. Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems, including vehicles in V2X networks. Rather than sharing and uploading the training data to the server, the updating of model parameters (e.g., neural networks' weights and biases) is applied by large populations of interconnected vehicles, acting as local learners. Despite these benefits, the …
MoE Enhanced Federated Learning for Spatiotemporal Prediction
Zhejiang University of Technology, Zhejiang Key Laboratory of Visual Information Intelligent Processing（浙江工业大学，浙江省可视信息智能处理重点实验室）；Jilin University（吉林大学）；City University of Hong Kong（香港城市大学）
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration
University Hospital Erlangen（埃尔朗根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN（埃尔朗根-EMN综合癌症中心）；Bavarian Cancer Research Center（巴伐利亚癌症研究中心）
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance
University of Electronic Science and Technology of China（电子科技大学）；Fudan University（复旦大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Meta AI；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Federated Learning with Differential Privacy: An Utility-Enhanced Approach
Deakin University（迪肯大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61）；The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；University of New South Wales (UNSW)（新南威尔士大学）
Federated learning (FL) has become a hot research area in enabling the collaborative training of machine learning models among multiple clients that hold sensitive local data. Nevertheless, unconstrained federated optimization has been studied mainly using stochastic gradient descent (SGD), which may converge slowly, and constrained federated optimization, which is more challenging, has not been investigated so far. This paper investigates sample-based and feature-based federated optimization, respectively, and considers both unconstrained and constrained nonconvex problems for each of them. F…
Towards Performance-Enhanced Model-Contrastive Federated Learning using Historical Information in Heterogeneous Scenarios
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences)（计算能力网络与信息安全重点实验室，教育部，山东计算机科学中心，齐鲁工业大学（山东科学院））；School of Information and Software Engineering, University of Electronic Science and Technology of China（信息与软件工程学院，电子科技大学）；School of Computer Science and Engineering, Shandong University of Science and Technology（计算机科学与工程学院，山东科技大学）；School of Big Data and Software Engineering, Chongqing University（大数据与软件工程学院，重庆大学）
