A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server. We present a comprehensive empirical study of the statistics of model updates in FL, as well as the role and benefits of various compression techniques. Motivated by these observations, we propose a novel method to reduce the average communication cost, which is near-optimal in many use cases, and outperforms Top-K, DRIVE, 3LC and QSGD on Stack Overflow next-word prediction, a realistic and challenging FL benchmark. This is achieved by examining the problem using rate-distortion theory, and proposing distortion as a reliable proxy for model accuracy. Distortion can be more effectively used for optimizing the trade-off between model performance and communication cost across clients. We demonstrate empirically that in spite of the non-i.i.d. nature of federated learning, the rate-distortion frontier is consistent across datasets, optimizers, clients and training rounds.
McMaster University（麦克马斯特大学）；National Yang-Ming Chiao-Tung University（国立阳明交通大学）；University of Toronto（多伦多大学）
In federated learning (FL), the communication constraint between the remote learners and the Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be compressed so as to minimize the loss in accuracy resulting from the communication constraint. This paper proposes ``\emph{${\bf M}$-magnitude weighted $L_{\bf 2}$ distortion + $\bf 2$ degrees of freedom''} (M22) algorithm, a rate-distortion inspired approach to gradient compression for federated training of deep neural networks (DNNs). In particular, we propose a family of distortion measures between the original gra…
Sejong University（世宗大学）；Pusan National University（釜山国立大学）
Recently, a considerable amount of works have been made to tackle the communication burden in federated learning (FL) (e.g., model quantization, data sparsification, and model compression). However, the existing methods, that boost the communication efficiency in FL, result in a considerable trade-off between communication efficiency and global convergence rate. We formulate an optimization problem for compression-aided FL, which captures the relationship between the distortion rate, number of participating IoT devices, and convergence rate. Following that, the objective function is to minimiz…
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
School of Computing and Informatics, University of Louisiana at Lafayette（路易斯安那大学拉法叶分校计算与信息学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；University of Delaware（特拉华大学）
Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Princeton University（普林斯顿大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Weizmann Institute of Science（魏茨曼科学研究所）
Traditional deep learning models are trained at a centralized server using labeled data samples collected from end devices or users. Such data samples often include private information, which the users may not be willing to share. Federated learning (FL) is an emerging approach to train such learning models without requiring the users to share their possibly private labeled data. In FL, each user trains its copy of the learning model locally. The server then collects the individual updates and aggregates them into a global model. A major challenge that arises in this method is the need of each…
University of Maryland（马里兰大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Numerous large-scale chest x-ray datasets have spearheaded expert-level detection of abnormalities using deep learning. However, these datasets focus on detecting a subset of disease labels that could be present, thus making them distributed and non-iid with partial labels. Recent literature has indicated the impact of batch normalization layers on the convergence of federated learning due to domain shift associated with non-iid data with partial labels. To that end, we propose FedFBN, a federated learning framework that draws inspiration from transfer learning by using pretrained networks as …
Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. The distributed operation of FL gives rise to challenges that are not encountered in centralized machine learning, including the need to preserve the privacy of the local datasets, and the communication load due to the repeated exchange of updated models. These challenges are often tackled individually via techniques that induce some distortion on the updated models, e.g., local differential privacy (LDP) mechanisms and lossy compression. In this work we p…
Missouri University of Science and Technology（密苏里科技大学）
The advances in satellite technology developments have recently seen a large number of small satellites being launched into space on Low Earth orbit (LEO) to collect massive data such as Earth observational imagery. The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS. Satellite edge computing (SEC), on the other hand, allows each satellite to train an ML model onboard and uploads only the model to the GS which appears to be a…
Federated learning (FL) empowers distributed clients to collaboratively train a shared machine learning model through exchanging parameter information. Despite the fact that FL can protect clients' raw data, malicious users can still crack original data with disclosed parameters. To amend this flaw, differential privacy (DP) is incorporated into FL clients to disturb original parameters, which however can significantly impair the accuracy of the trained model. In this work, we study a crucial question which has been vastly overlooked by existing works: what are the optimal numbers of queries a…
Optimizing Federated Learning by Entropy-Based Client Selection
Department of Artificial Intelligence, Fraunhofer Heinrich-Hertz-Institute（人工智能系，弗劳恩霍夫海因里希·赫兹研究所）；Department of Mathematics, Technische Universität Berlin（数学系，柏林技术大学）；Department of Electrical Engineering and Computer Science, Technische Universität Berlin（电气工程与计算机科学系，柏林技术大学）；BIFOLD – Berlin Institute for the Foundations of Learning（BIFOLD——柏林学习与数据基础研究所）
School of Electrical and Computer Engineering, Purdue University（普渡大学电气与计算机工程学院）
Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks. This work addresses an important consideration of federated learning at the network edge: communication delays between the edge nodes and the aggregator. A technique called FedDelAvg (federated delayed averaging) is developed, which generalizes the standard federated averaging algorithm to incorporate a weighting between the current local model and the delayed global model received at each device during the synchronization step. Through theo…
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University（深圳大学广东省人工智能与数字经济实验室（SZ））；School of Computing, Faculty of Science and Engineering, Macquarie University（麦考瑞大学科学与工程学院计算学院）；Institute for Network Sciences and Cyberspace, Tsinghua University（清华大学网络科学与网络空间研究院）；School of Computing Science, Simon Fraser University（西蒙菲莎大学计算科学学院）
Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids
Department of Computational and Data Sciences, Indian Institute of Science (IISc), Bangalore, India（计算与数据科学系，印度科学研究院（IISc），班加罗尔，印度）；Department of Electrical Engineering, Indian Institute of Science (IISc), Bangalore, India（电气工程系，印度科学研究院（IISc），班加罗尔，印度）；Accenture, India（印度德勤）
Optimizing Asynchronous Federated Learning: A Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency
EMINES, Mohammed VI Polytechnic University, Ben Guerir, Morocco（EMINES，摩洛哥穆莱·易斯大学，贝努埃里尔）；LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France（LAAS-CNRS，图卢兹大学，CNRS，图卢兹）
University of Iowa（爱荷华大学）；University of Virginia（弗吉尼亚大学）
We propose a federated learning method with weighted nodes in which the weights can be modified to optimize the model's performance on a separate validation set. The problem is formulated as a bilevel optimization where the inner problem is a federated learning problem with weighted nodes and the outer problem focuses on optimizing the weights based on the validation performance of the model returned from the inner problem. A communication-efficient federated optimization algorithm is designed to solve this bilevel optimization problem. Under an error-bound assumption, we analyze the generaliz…
DP-EMAR: A Differentially Private Framework for Autonomous Model Weight Repair in Federated IoT Systems
Research Scholar, Department of CSE（计算机科学与工程系研究学者）；Professor, Department of CSE（计算机科学与工程系教授）；PES University, RR Nagar, Bangalore, India（印度班加罗尔RR Nagar的PES大学）
FedOptima: Optimizing Resource Utilization in Federated Learning
organization= School of Computer Science, University of St Andrews , addressline= Jack Cole Building , city= St Andrews , postcode= KY16 9SX , state= Fife , country= Scotland, United Kingdom；organization= Autonomous Networking Research \& Innovation Department, Rakuten Mobile, Inc. , addressline= Rakuten Crimson House, 1-14-1 Tamagawa, Setagaya-ku , city= Tokyo , postcode= 158-0094 , state= Tokyo , country= Japan
Jeonbuk National University（全北国立大学）；Pukyong National University（釜庆国立大学）
This work studies federated learning (FL) over a fog radio access network, in which multiple internet-of-things (IoT) devices cooperatively learn a shared machine learning model by communicating with a cloud server (CS) through distributed access points (APs). Under the assumption that the fronthaul links connecting APs to CS have finite capacity, a rate-splitting transmission at IoT devices (IDs) is proposed which enables hybrid edge and cloud decoding of split uplink messages. The problem of completion time minimization for FL is tackled by optimizing the rate-splitting transmission and fron…
Federated learning is a rapidly-growing area of research which enables a large number of clients to jointly train a machine learning model on privately-held data. One of the largest barriers to wider adoption of federated learning is the communication cost of sending model updates from and to the clients, which is accentuated by the fact that many of these devices are bandwidth-constrained. In this paper, we aim to address this issue by optimizing networks within a subspace of their full parameter space, an idea known as intrinsic dimension in the machine learning theory community. We use a co…
Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless mobile devices, the communication overhead is a critical bottleneck due to limited power and bandwidth. Prior work has utilized various data compression tools such as quantization and sparsification to reduce the overhead. In this paper, we propose a predictive coding based compression scheme for federated learning. The scheme has shared prediction functions among all devices and allows each worker to transmit a compr…
Ericsson Research（爱立信研究院）；Ericsson Security Research（爱立信安全研究院）；KTH Royal Institute of Technology（瑞典皇家理工学院）
Machine Learning (ML) is an important enabler for optimizing, securing and managing mobile networks. This leads to increased collection and processing of data from network functions, which in turn may increase threats to sensitive end-user information. Consequently, mechanisms to reduce threats to end-user privacy are needed to take full advantage of ML. We seamlessly integrate Federated Learning (FL) into the 3GPP 5G Network Data Analytics (NWDA) architecture, and add a Multi-Party Computation (MPC) protocol for protecting the confidentiality of local updates. We evaluate the protocol and fin…
A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment - Strategy Update
University Medical Center Göttingen（哥廷根大学医学中心）；University of Göttingen（哥廷根大学）；Justus-Liebig University（吉森尤斯图斯-李比希大学）；Technical University of Munich（慕尼黑工业大学）；TUM School of Medicine and Health（慕尼黑工业大学医学与健康学院）；Technical University of Denmark（丹麦技术大学）；Max Planck Institute for Biology of Ageing（马克斯·普朗克衰老生物学研究所）；Philipps-University Marburg（马尔堡菲利普斯大学）
Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server. While it is theoretically well-known that FL yields an optimal model -- centrally trained model assuming availability of all the edge device data at the central server -- under mild condition, in practice, it often requires massive amount of iterations until convergence, especially under presence of statistical/computational heterogeneity. This paper utilizes cyclic learning rate at the server side to reduce the number of training iterations wit…
University of Washington（华盛顿大学）；Google Research（谷歌研究院）
Federated Learning (FL) refers to learning a high quality global model based on decentralized data storage, without ever copying the raw data. A natural scenario arises with data created on mobile phones by the activity of their users. Given the typical data heterogeneity in such situations, it is natural to ask how can the global model be personalized for every such device, individually. In this work, we point out that the setting of Model Agnostic Meta Learning (MAML), where one optimizes for a fast, gradient-based, few-shot adaptation to a heterogeneous distribution of tasks, has a number o…
AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems
Department of Electrical Engineering, Chalmers University of Technology（楚德大学电气工程系）；School of Electrical and Computer Engineering, Purdue University（普渡大学电气与计算机工程学院）
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）
Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server. Two natural challenges that FL algorithms face are heterogeneity in data across clients and collaboration of clients with {\em diverse resources}. In this work, we introduce a \textit{quantized} and \textit{personalized} FL algorithm QuPeL that facilitates collective training with heterogeneous clients while respecting resource diversity. For personalization, we allow clients to learn \textit{compressed personalized models} with different quantization parameters…
Nanyang Technological University（南洋理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Sungkyunkwan University（成均馆大学）
To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e.g., mobile devices, to distributedly train and globally share models without revealing their local data. This technique can not only significantly improve privacy protection for mobile devices, but also ensure good performance of the trained results collectively. Currently, most the existing studies focus on optimizing federated learning algorithms to improve model training performance. However, incentive mechanisms to motivate the mobile devices to j…
This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data. In such wireless federated learning networks (WFLNs), optimizing the learning performance depends crucially on how clients are selected and how bandwidth is allocated among the selected clients in every learning round, as both radio and client energy resources are limited. While existing works have made some attempts to allocate the limited wireless resources to optimize FL, they focus …
Federated Learning (FL) is a learning paradigm that protects privacy by keeping client data on edge devices. However, optimizing FL in practice can be difficult due to the diversity and heterogeneity of the learning system. Despite recent research efforts to improve the optimization of heterogeneous data, the impact of time-evolving heterogeneous data in real-world scenarios, such as changing client data or intermittent clients joining or leaving during training, has not been studied well. In this work, we propose Continual Federated Learning (CFL), a flexible framework for capturing the time-…
Federated learning (FL) is a promising technique that enables many edge devices to train a machine learning model collaboratively in wireless networks. By exploiting the superposition nature of wireless waveforms, over-the-air computation (AirComp) can accelerate model aggregation and hence facilitate communication-efficient FL. Due to channel fading, power control is crucial in AirComp. Prior works assume that the signals to be aggregated from each device, i.e., local gradients have identical statistics. In FL, however, gradient statistics vary over both training iterations and feature dimens…
Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a central server. In this paper, we consider a 3-layer hierarchical federated learning system which involves model parameter exchanges between the cloud and edge servers, and the edge servers and user equipment. In a hierarchical federated learning model, delay in communication and computation of model parameters has a great impact on achieving a predefined global model accuracy. Therefore, we formulate a joint learning and…
Naval Information Warfare Center Pacific（太平洋海军信息战中心）
In this work, we explore combining automatic hyperparameter tuning and optimization for federated learning (FL) in an online, one-shot procedure. We apply a principled approach on a method for adaptive client learning rate, number of local steps, and batch size. In our federated learning applications, our primary motivations are minimizing communication budget as well as local computational resources in the training pipeline. Conventionally, hyperparameter tuning methods involve at least some degree of trial-and-error, which is known to be sample inefficient. In order to address our motivation…
