Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model. Due to privacy concerns, the raw data on devices could not be available for centralized server. Constrained by the spectrum limitation and computation capacity, only a subset of devices can be engaged to train and transmit the trained model to centralized server for aggregation. Since the local data distribution varies among all devices, class imbalance problem arises along with the unfavorable client selection, resulting in a slow converge rate of the global model. In this paper, an estimation scheme is designed to reveal the class distribution without the awareness of raw data. Based on the scheme, a device selection algorithm towards minimal class imbalance is proposed, thus can improve the convergence performance of the global model. Simulation results demonstrate the effectiveness of the proposed algorithm.
Northeastern University（东北大学）；The University of Melbourne（墨尔本大学）；RMIT University（皇家墨尔本理工大学）
Federated learning, which allows multiple client devices in a network to jointly train a machine learning model without direct exposure of clients' data, is an emerging distributed learning technique due to its nature of privacy preservation. However, it has been found that models trained with federated learning usually have worse performance than their counterparts trained in the standard centralized learning mode, especially when the training data is imbalanced. In the context of federated learning, data imbalance may occur either locally one one client device, or globally across many device…
The Federated Learning setting has a central server coordinating the training of a model on a network of devices. One of the challenges is variable training performance when the dataset has a class imbalance. In this paper, we address this by introducing a new loss function called Fed-Focal Loss. We propose to address the class imbalance by reshaping cross-entropy loss such that it down-weights the loss assigned to well-classified examples along the lines of focal loss. Additionally, by leveraging a tunable sampling framework, we take into account selective client model contributions on the ce…
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Huazhong University of Science and Technology（华中科技大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Hong Kong University of Science and Technology（香港科技大学）
Federated learning is a distributed machine learning paradigm that trains a global model for prediction based on a number of local models at clients while local data privacy is preserved. Class imbalance is believed to be one of the factors that degrades the global model performance. However, there has been very little research on if and how class imbalance can affect the global performance. class imbalance in federated learning is much more complex than that in traditional non-distributed machine learning, due to different class imbalance situations at local clients. Class imbalance needs to …
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare
Indian Institute of Technology, Bombay（印度理工学院孟买分校）；Sardar Patel Institute of Technology（萨达尔·帕特尔理工学院）；Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
Washington State University（华盛顿州立大学）；Princeton University（普林斯顿大学）；IBM Research（IBM研究院）；University of Florida（佛罗里达大学）；University of California San Diego（加州大学圣迭戈分校）
Peking University（北京大学）；Key Laboratory of Machine Perception (Ministry of Education), Peking University（北京大学机器感知重点实验室（教育部））；National Institute of Health Data Science, Peking University（北京大学健康数据科学研究院）；Institute of Medical Technology, Health Science Center of Peking University（北京大学医学部医学技术研究院）
Since data is presented long-tailed in reality, it is challenging for Federated Learning (FL) to train across decentralized clients as practical applications. We present Global-Regularized Personalization (GRP-FED) to tackle the data imbalanced issue by considering a single global model and multiple local models for each client. With adaptive aggregation, the global model treats multiple clients fairly and mitigates the global long-tailed issue. Each local model is learned from the local data and aligns with its distribution for customization. To prevent the local model from just overfitting, …
Fast Convergence through Distributed Augmentation for Class-Imbalanced Federated Learning
Amar Nath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi（印度理工学院德里分校 Amar Nath and Shashi Khosla 信息技术学院）；Department of Electrical Engineering, Indian Institute of Technology Delhi（印度理工学院德里分校电气工程系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Chongqing University（重庆大学）；Beijing Institute of Control Engineering（北京控制工程研究所）
Federated learning (FL) is a distributed deep learning method which enables multiple participants, such as mobile phones and IoT devices, to contribute a neural network model while their private training data remains in local devices. This distributed approach is promising in the edge computing system where have a large corpus of decentralized data and require high privacy. However, unlike the common training dataset, the data distribution of the edge computing system is imbalanced which will introduce biases in the model training and cause a decrease in accuracy of federated learning applicat…
School of Management Engineering, Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院管理工程学院）
In clinical research, the lack of events of interest often necessitates imbalanced learning. One approach to resolve this obstacle is data integration or sharing, but due to privacy concerns neither is practical. Therefore, there is an increasing demand for a platform on which an analysis can be performed in a federated environment while maintaining privacy. However, it is quite challenging to develop a federated learning algorithm that can address both privacy-preserving and class imbalanced issues. In this study, we introduce a federated generative model learning platform for generating samp…
Mitigating Participation Imbalance Bias in Asynchronous Federated Learning
University of California, Riverside（加州大学河滨分校）；Indian Institute of Science（印度科学研究院）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；DEVCOM Army Research Laboratory（陆军研究实验室）
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）
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（应用数学与理论物理系，剑桥大学）；Department of Radiology, University of Cambridge（放射学系，剑桥大学）
Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution and quantity of the training data on the clients' side may lead to significant challenges such as class imbalance and non-IID (non-independent and identically distributed) data, which could greatly impact the performance of the common model. While much effort has been devoted to helping FL models converge when encountering non-IID data, the imbalance issue has not been sufficiently addressed. In particular, as FL train…
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
The Pennsylvania State University（宾夕法尼亚州立大学）；State University of New York at Albany（纽约州立大学阿尔巴尼分校）；University of Minnesota Twin Cities（明尼苏达大学双城分校）；Dalian University of Technology（大连理工大学）
FedReLa: Imbalanced Federated Learning via Re-Labeling
School of Computing and Information Systems, University of Melbourne, Victoria, Australia（墨尔本大学计算机与信息系统学院）；School of Statistics and Data Science, LPMC, KLMDASR, and LEBPS, Nankai University（南开大学统计与数据科学学院）；School of Mathematics and Statistics, University of Melbourne, Victoria, Australia（墨尔本大学数学与统计学学院）
Qatar University（卡塔尔大学）；Qualcomm Inc（高通公司）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches. Despite the abundance of such advantages, it is imperative to investigate the performance of UAV utilization while considering their design limitations. This paper investigates the deployment of UAV swarms when each UAV carries a machine learning classification task. To avoid data exchange with ground-based processing nodes, a federated learning approach is adopted between a UAV leader and the swarm members to impro…
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data
Computer Engineering Department, İstanbul Commerce University（伊斯坦布尔商业大学计算机工程系）；Turkcell Teknoloji（Turkcell技术公司）；Grid Communications and Security Group, Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory（橡树岭国家实验室电网安全与能源基础设施部门）
针对联邦学习中的噪声标签、类别缺失与不均衡问题，提出结合自适应噪声清洗、条件 GAN 合成补全和鲁棒训练的隐私保护框架，并在 MNIST 与 Fashion-MNIST 上验证其性能提升。
With the ever increasing data deluge and the success of deep neural networks, the research of distributed deep learning has become pronounced. Two common approaches to achieve this distributed learning is synchronous and asynchronous weight update. In this manuscript, we have explored very simplistic synchronous weight update mechanisms. It has been seen that with an increasing number of worker nodes, the performance degrades drastically. This effect has been studied in the context of extreme imbalanced classification (e.g. outlier detection). In practical cases, the assumed conditions of i.i.…
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Duke University（杜克大学）；University of Maryland, College Park（马里兰大学帕克分校）；George Mason University（乔治梅森大学）；Yale University（耶鲁大学）；The State University of New York at Buffalo（纽约州立大学布法罗分校）
Federated learning (FL) aided health diagnostic models can incorporate data from a large number of personal edge devices (e.g., mobile phones) while keeping the data local to the originating devices, largely ensuring privacy. However, such a cross-device FL approach for health diagnostics still imposes many challenges due to both local data imbalance (as extreme as local data consists of a single disease class) and global data imbalance (the disease prevalence is generally low in a population). Since the federated server has no access to data distribution information, it is not trivial to solv…
Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on heterogeneous systems due to the various computational capabilities and communication bottlenecks. In this work, we propose FedSkel to enable computation-efficient and communication-efficient federated learning on edge devices by only updating the model's essential parts, named skeleton networks. FedSkel is evaluated on real edge devices with imbalanced datasets. Experimental results show that it could achieve up to 5.5…
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
The Hong Kong University of Science and Technology（香港科技大学）；SenseTime Research（商汤科技研究院）；Sony AI（索尼人工智能）；The Hong Kong Polytechnic University（香港理工大学）；Northwestern Polytechnical University（西北工业大学）
Federated learning enables many local devices to train a deep learning model jointly without sharing the local data. Currently, most of federated training schemes learns a global model by averaging the parameters of local models. However, most of these training schemes suffer from high communication cost resulted from transmitting full local model parameters. Moreover, directly averaging model parameters leads to a significant performance degradation, due to the class-imbalanced non-iid data on different devices. Especially for the real life federated learning tasks involving extreme classific…
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；American University of Beirut（贝鲁特美国大学）；Qatar University（卡塔尔大学）
Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection, for detection, classification, and prediction of future events, from different locations while maintaining a strict privacy constraint. Due to privacy concerns and critical communication bottlenecks, it can become impractical to send the FL updated models to a centralized server. Thus, this paper studies the potential of hiera…
Zhejiang University（浙江大学）；Singapore University of Technology and Design（新加坡科技设计大学）
Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks. Recent attempts have been launched to, on one side, address the problem of learning from pervasive private data, and on the other side, learn from long-tailed data. However, both assumptions might hold in practical applications, while an effective method to simultaneously alleviate both issues is yet under development. In this paper, we focus on learning with long-tailed (LT) data distributions under the context of the popular privacy-preserved federated learning (FL) framework. We character…
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
National Engineering Laboratory for Big Data Analytics (NEL-BDA)（大数据分析国家工程实验室）；School of Mathematics and Statistics（数学与统计学学院）；Ministry of Education Key Lab for Intelligent Networks and Network Security（教育部智能网络与网络安全重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）
Federated learning (FL) is an emerging technique used to collaboratively train a global machine learning model while keeping the data localized on the user devices. The main obstacle to FL's practical implementation is the Non-Independent and Identical (Non-IID) data distribution across users, which slows convergence and degrades performance. To tackle this fundamental issue, we propose a method (ComFed) that enhances the whole training process on both the client and server sides. The key idea of ComFed is to simultaneously utilize client-variance reduction techniques to facilitate server aggr…
Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；Center for Applied Statistics, School of Statistics, Renmin University of China（中国人民大学统计学院应用统计中心）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）；Department of Statistics, Fudan University（复旦大学统计系）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
The Future Network of Intelligence Institute（未来网络智能研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Zhejiang University（浙江大学）；Peng Cheng Laboratory（鹏城实验室）
Rethinking Personalized Reward Modeling for LLMs under Preference Heterogeneity via Group-Debiased Federated Learning
Institute of Engineering Research, Korea University（韩国大学工程研究所）；Kim Jaechul Graduate School of AI, KAIST（韩国科学技术院金在哲人工智能研究生院）；Industrial Management Engineering, Korea University（韩国大学产业管理工程学院）
