Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application
College of Computing and Informatics, Sungkyunkwan University（成均馆大学计算与信息学院）；Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute（电子通信研究院生物医学IT融合研究部）；Samsung Medical Center（三星医疗中心）
Joo Hun Yoo, Ha Min Son, Hyejun Jeong, Eun-Hye Jang, Ah Young Kim, Han Young Yu, Hong Jin Jeon, Tai-Myoung Chung
While machine learning techniques are being applied to various fields for their exceptional ability to find complex relations in large datasets, the strengthening of regulations on data ownership and privacy is causing increasing difficulty in its application to medical data. In light of this, Federated Learning has recently been proposed as a solution to train on private data without breach of confidentiality. This conservation of privacy is particularly appealing in the field of healthcare, where patient data is highly confidential. However, many studies have shown that its assumption of Independent and Identically Distributed data is unrealistic for medical data. In this paper, we propose Personalized Federated Cluster Models, a hierarchical clustering-based FL process, to predict Major Depressive Disorder severity from Heart Rate Variability. By allowing clients to receive more personalized model, we address problems caused by non-IID data, showing an accuracy increase in severity prediction. This increase in performance may be sufficient to use Personalized Federated Cluster Models in many existing Federated Learning scenarios.
Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models. In these modeling approaches, data needs to be gathered to accurately train models, at the same time that user's privacy is maintained. An appealing scheme to cooperatively achieve these goals is known as Federated Learning (FL). A challenge in FL schemes is the presence of non-independent and identically distributed (non-IID) data, caused by unevenly exploration of different areas. In this paper, we consider the use of re…
FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data
Department of Data Science and AI, Faculty of IT and Monash Energy Institute, Monash University（数据科学与人工智能系，IT学院和莫纳什能源研究所，莫纳什大学）；School of Computer Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, and Shenzhen Institute for Advanced Study of UESTC, Shenzhen, China（计算机科学与技术学院，电子科技大学（UESTC）成都校区，以及UESTC深圳高级研究所，深圳，中国）
Simon Fraser University（西蒙弗雷泽大学）；McMaster University（麦克马斯特大学）；Huawei Technologies Canada（华为技术加拿大公司）
Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish the convergence of FedAMP for both convex and non-convex models, and propose a heuristic method to further improve the performance of FedAMP when clients adopt deep neural networks as personalized models. Our extensive experiments on benchmark data sets demonstrate the superior pe…
Duke University（杜克大学）；Alibaba DAMO Academy（阿里巴巴达摩院）
Federated learning is a popular distributed machine learning paradigm with enhanced privacy. Its primary goal is learning a global model that offers good performance for the participants as many as possible. The technology is rapidly advancing with many unsolved challenges, among which statistical heterogeneity (i.e., non-IID) and communication efficiency are two critical ones that hinder the development of federated learning. In this work, we propose LotteryFL -- a personalized and communication-efficient federated learning framework via exploiting the Lottery Ticket hypothesis. In LotteryFL,…
Clust-PSI-PFL: A Population Stability Index Approach for Clustered Non-IID Personalized Federated Learning
Department of Computer, Control；Management Engineering Sapienza University of Rome Rome, Italy；Telef\'onica Research Barcelona, Spain；Keysight AI Labs Barcelona, Spain
Federated learning (FL) facilitates multiple clients to jointly train a machine learning model without sharing their private data. However, Non-IID data of clients presents a tough challenge for FL. Existing personalized FL approaches rely heavily on the default treatment of one complete model as a basic unit and ignore the significance of different layers on Non-IID data of clients. In this work, we propose a new framework, federated model components self-attention (FedMCSA), to handle Non-IID data in FL, which employs model components self-attention mechanism to granularly promote cooperatio…
Personalized Federated Dictionary Learning for Modeling Heterogeneity in Multi-site fMRI Data
Tulane Center for Biomedical Informatics and Genomics（Tulane生物医学信息学与基因组学中心）；School of Medicine, Tulane University（Tulane大学医学院）；State University of New York at Oswego（纽约州立大学奥尔巴尼分校）
UA-PDFL: A Personalized Approach for Decentralized Federated Learning
School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）；Jiangsu Key University Laboratory of Software and Media Technology under Human-Computer Cooperation (Jiangnan University)（江南大学江苏省人机协同软件与媒体技术重点实验室）
Personalized federated learning (FL) facilitates collaborations between multiple clients to learn personalized models without sharing private data. The mechanism mitigates the statistical heterogeneity commonly encountered in the system, i.e., non-IID data over different clients. Existing personalized algorithms generally assume all clients volunteer for personalization. However, potential participants might still be reluctant to personalize models since they might not work well. In this case, clients choose to use the global model instead. To avoid making unrealistic assumptions, we introduce…
Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；University of Washington, Seattle（西雅图华盛顿大学）；University of Maryland, College Park（马里兰大学帕克分校）
Knowledge sharing and model personalization are essential components to tackle the non-IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1) to learn a shared model to serve all clients with non-IID data, and 2) to learn personalized models for each client, namely personalized FL. There is a trade-off solution, namely clustered FL or cluster-wise personalized FL, which aims to cluster similar clients into one cluster, and then learn a shared model for all clients within a cluster. This paper is to revisit the research of clustered FL by formulating them i…
Conventional federated learning (FL) trains one global model for a federation of clients with decentralized data, reducing the privacy risk of centralized training. However, the distribution shift across non-IID datasets, often poses a challenge to this one-model-fits-all solution. Personalized FL aims to mitigate this issue systematically. In this work, we propose APPLE, a personalized cross-silo FL framework that adaptively learns how much each client can benefit from other clients' models. We also introduce a method to flexibly control the focus of training APPLE between global and local ob…
Classical federated learning approaches yield significant performance degradation in the presence of Non-IID data distributions of participants. When the distribution of each local dataset is highly different from the global one, the local objective of each client will be inconsistent with the global optima which incur a drift in the local updates. This phenomenon highly impacts the performance of clients. This is while the primary incentive for clients to participate in federated learning is to obtain better personalized models. To address the above-mentioned issue, we present a new algorithm…
Federated learning aims to collaboratively train models without accessing their client's local private data. The data may be Non-IID for different clients and thus resulting in poor performance. Recently, personalized federated learning (PFL) has achieved great success in handling Non-IID data by enforcing regularization in local optimization or improving the model aggregation scheme on the server. However, most of the PFL approaches do not take into account the unfair competition issue caused by the imbalanced data distribution and lack of positive samples for some classes in each client. To …
FedBiCross: Personalized One-Shot Federated Learning on Medical Images
School of Computer Science and Engineering, Northwestern Polytechnical University, China（西北工业大学计算机科学与工程学院）；School of Science and Technology, Hong Kong Metropolitan University, Hong Kong（香港 Metropolitan 大学科学与技术学院）；Department of Computer Science, Hong Kong Baptist University, Hong Kong（香港 Baptist 大学计算机科学系）
RISE Research Institutes of Sweden（瑞典RISE研究院）；Chalmers University of Technology（查尔姆斯理工大学）；KTH Royal Institute of Technology（KTH皇家理工学院）
We tackle the non-convex problem of learning a personalized deep learning model in a decentralized setting. More specifically, we study decentralized federated learning, a peer-to-peer setting where data is distributed among many clients and where there is no central server to orchestrate the training. In real world scenarios, the data distributions are often heterogeneous between clients. Therefore, in this work we study the problem of how to efficiently learn a model in a peer-to-peer system with non-iid client data. We propose a method named Performance-Based Neighbor Selection (PENS) where…
The Hong Kong Polytechnic University（香港理工大学）；Beijing Institute of Technology（北京理工大学）
Facing the challenge of statistical diversity in client local data distribution, personalized federated learning (PFL) has become a growing research hotspot. Although the state-of-the-art methods with model similarity-based pairwise collaboration have achieved promising performance, they neglect the fact that model aggregation is essentially a collaboration process within the coalition, where the complex multiwise influences take place among clients. In this paper, we first apply Shapley value (SV) from coalition game theory into the PFL scenario. To measure the multiwise collaboration among a…
University of Technology Sydney（悉尼科技大学）；Monash University（莫纳什大学）；Data61, CSIRO（联邦科学与工业研究组织Data61研究所）
Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing to the advantages of federated learning, federated graph learning (FGL) enables clients to train strong GNN models in a distributed manner without sharing their private data. A core challenge in federated systems is the non-IID problem, which also widely exists in real-world graph data. For example, local data of clients may come from diverse datasets or even domains, e.g., social networks and molecules, increasing the difficulty for FGL methods to capture commonly shared knowledge and learn a generalized e…
In Federated Learning (FL), the clients learn a single global model (FedAvg) through a central aggregator. In this setting, the non-IID distribution of the data across clients restricts the global FL model from delivering good performance on the local data of each client. Personalized FL aims to address this problem by finding a personalized model for each client. Recent works widely report the average personalized model accuracy on a particular data split of a dataset to evaluate the effectiveness of their methods. However, considering the multitude of personalization approaches proposed, it …
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；State University of New York at Buffalo（纽约州立大学布法罗分校）
pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning
Saunders College of Business, Rochester Institute of Technology（罗切斯特理工学院商学院）；School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电子与计算机工程学院）
Federated Learning (FL) has recently emerged as a promising method that employs a distributed learning model structure to overcome data privacy and transmission issues paused by central machine learning models. In FL, datasets collected from different devices or sensors are used to train local models (clients) each of which shares its learning with a centralized model (server). However, this distributed learning approach presents unique learning challenges as the data used at local clients can be non-IID (Independent and Identically Distributed) and statistically diverse which decrease learnin…
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Jindal School of Management, The University of Texas at Dallas（德克萨斯大学达拉斯分校金德尔管理学院）；Department of Statistics, The University of Chicago（芝加哥大学统计系）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Republic of Korea（人工智能跨学科项目，首尔国立大学，韩国）；Department of Nuclear Medicine, Seoul National University College of Medicine, Republic of Korea（核医学系，首尔国立大学医学院，韩国）；Brightonix Imaging Inc. Seoul, Republic of Korea（Brightonix影像公司，首尔，韩国）
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Deakin University（迪肯大学）；Jilin University（吉林大学）；Changchun University of Technology（长春工业大学）；Macquarie University（麦考瑞大学）；Qilu University of Technology（齐鲁工业大学）；Shandong Computer Science Center（山东计算机科学中心）；Facebook（脸书）
Zhejiang University（浙江大学）；Zhejiang Lab（之江实验室）；Huazhong University of Science and Technology（华中科技大学）
Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing about distinctive challenges to the canonical federated learning algorithm (FedAvg). First, due to the Non-IIDness of data, the global shared model may perform worse than local models that solely trained on their private data; Second, the objective of center server and clients may be different, where center server seeks for a generalized model whereas client pursue a personalized model, and clients may run different tasks;…
Federated learning (FL) is recently surging as a promising decentralized deep learning (DL) framework that enables DL-based approaches trained collaboratively across clients without sharing private data. However, in the context of the central party being active and dishonest, the data of individual clients might be perfectly reconstructed, leading to the high possibility of sensitive information being leaked. Moreover, FL also suffers from the nonindependent and identically distributed (non-IID) data among clients, resulting in the degradation in the inference performance on local clients' dat…
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；State University of New York at Buffalo（纽约州立大学布法罗分校）
Federated learning (FL) has emerged as an effective solution to decentralized and privacy-preserving machine learning for mobile clients. While traditional FL has demonstrated its superiority, it ignores the non-iid (independently identically distributed) situation, which widely exists in mobile scenarios. Failing to handle non-iid situations could cause problems such as performance decreasing and possible attacks. Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models. However, previous …
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
Beijing University of Posts and Telecommunications（北京邮电大学）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Centre for Frontier AI Research, Agency for Science, Technology and Research（新加坡科技研究局前沿人工智能研究中心）；School of Engineering, Westlake University（西湖大学工程学院）
