With growth in the number of smart devices and advancements in their hardware, in recent years, data-driven machine learning techniques have drawn significant attention. However, due to privacy and communication issues, it is not possible to collect this data at a centralized location. Federated learning is a machine learning setting where the centralized location trains a learning model over remote devices. Federated learning algorithms cannot be employed in the real world scenarios unless they consider unreliable and resource-constrained nature of the wireless medium. In this paper, we propose a federated learning algorithm that is suitable for cellular wireless networks. We prove its convergence, and provide the optimal scheduling policy that maximizes the convergence rate. We also study the effect of local computation steps and communication steps on the convergence of the proposed algorithm. We prove, in practice, federated learning algorithms may solve a different problem than the one that they have been employed for if the unreliability of wireless channels is neglected. Finally, through numerous experiments on real and synthetic datasets, we demonstrate the convergence of our proposed algorithm.
Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks
School of Computer Science and Technology, Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）；University of Electronic Science and Technology of China（电子科技大学）
Nanjing University of Science and Technology（南京理工大学）；Data61, CSIRO（联邦科学与工业研究组织数据61研究所）；Shanghai Jiao Tong University（上海交通大学）；Princeton University（普林斯顿大学）
Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, with the distributed architecture, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. In this paper, we model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk. Specifically, we first investigate the impact on the models caused by unreliable clie…
National Yang Ming Chiao Tung University（国立阳明交通大学）；Mississippi State University（密西西比州立大学）；Texas Tech University（德克萨斯理工大学）
Federated learning (FL) is a promising distributed learning technique particularly suitable for wireless learning scenarios since it can accomplish a learning task without raw data transportation so as to preserve data privacy and lower network resource consumption. However, current works on FL over wireless networks do not profoundly study the fundamental performance of FL over wireless networks that suffers from communication outage due to channel impairment and network interference. To accurately exploit the performance of FL over wireless networks, this paper proposes a novel intermittent …
Missouri University of Science and Technology（密苏里科技大学）；Singapore University of Technology and Design（新加坡科技设计大学）
Federated learning offers a framework of training a machine learning model in a distributed fashion while preserving privacy of the participants. As the server cannot govern the clients' actions, nefarious clients may attack the global model by sending malicious local gradients. In the meantime, there could also be unreliable clients who are benign but each has a portion of low-quality training data (e.g., blur or low-resolution images), thus may appearing similar as malicious clients. Therefore, a defense mechanism will need to perform a three-fold differentiation which is much more challengi…
Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach
School of Computer Science, Nanjing University of Posts and Telecommunications（南京邮电大学计算机科学学院）；School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）人工智能学院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data
Beijing University of Posts and Telecommunications（北京邮电大学）；Jiaxing University（嘉兴学院）；Northwestern Polytechnical University（西北工业大学）；Southeast University（东南大学）
Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua University（清华大学）；RISC-V International Open Source Laboratory（RISC-V国际开源实验室）；National Mobile Communications Research Laboratory（移动通信国家重点实验室）；Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；George Washington University（乔治华盛顿大学）；Ryerson University（瑞尔森大学）
Federated learning (FL) enables edge devices to collaboratively learn a model in a distributed fashion. Many existing researches have focused on improving communication efficiency of high-dimensional models and addressing bias caused by local updates. However, most of FL algorithms are either based on reliable communications or assume fixed and known unreliability characteristics. In practice, networks could suffer from dynamic channel conditions and non-deterministic disruptions, with time-varying and unknown characteristics. To this end, in this paper we propose a sparsity enabled FL framewo…
Heilongjiang University（黑龙江大学）；Nanyang Technological University（南洋理工大学）；Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹亲王大学）；Tokyo Institute of Technology（东京工业大学）；Changchun University of Science and Technology（长春理工大学）；Menoufia University（梅努菲亚大学）；Nile University（尼罗河大学）
Federated Learning (FL) has been recently proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global mod…
Imperial College London（帝国理工学院）；Sabanci University（萨班哲大学）
We study collaborative machine learning (ML) across wireless devices, each with its own local dataset. Offloading these datasets to a cloud or an edge server to implement powerful ML solutions is often not feasible due to latency, bandwidth and privacy constraints. Instead, we consider federated edge learning (FEEL), where the devices share local updates on the model parameters rather than their datasets. We consider a heterogeneous cellular network (HCN), where small cell base stations (SBSs) orchestrate FL among the mobile users (MUs) within their cells, and periodically exchange model updat…
Kyung Hee University（庆熙大学）；The University of Sydney（悉尼大学）；University of Houston（休斯顿大学）
Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine learning model, and then send the trained models to the BS, which generates the initial model, collect…
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks
Democritus University of Thrace（色雷斯德谟克利特大学）；Imperial College London（伦敦帝国理工学院）；Nokia Bell Labs（诺基亚贝尔实验室）；Centre Tecnològic de Telecomunicacions de Catalunya（加泰罗尼亚电信技术中心）；IMT Atlantique（大西洋国立高等矿业电信学校）；Inria（法国国家信息与自动化研究所）
To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need to fulfill their missions in the shortest possible time. In addition to this requirement, in many applications, the UAVs require a reliable internet connection during their flights. In this paper, we minimize the travel time of the UAVs, ensuring that a probabilistic connectivity constraint is satisfied. To solve this problem, we need a global model of the outage probability in the environment. Since the UAVs have different missions and fly over different areas, their collected data carry local information on the net…
Royal Military College of Canada（加拿大皇家军事学院）；Ryerson University（瑞尔森大学）
In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief. Regarding the cybersecurity of FL, some attacks aim at injecting the fabricated local model updates into the system. Some defenses are based on malicious worker detection and behavioral pattern analysis. In this context, without timely and dynamic monitoring methods, the chief cannot detect and remove the malicious or unreliable workers from the system. Our work emphasize the urgency to prepare the federated learning process for monitoring and eventually behavioral …
Splunk Inc.（Splunk公司）；University of Illinois（伊利诺伊大学）
Federated Learning is a rapidly growing area of research and with various benefits and industry applications. Typical federated patterns have some intrinsic issues such as heavy server traffic, long periods of convergence, and unreliable accuracy. In this paper, we address these issues by proposing asynchronous hierarchical federated learning, in which the central server uses either the network topology or some clustering algorithm to assign clusters for workers (i.e., client devices). In each cluster, a special aggregator device is selected to enable hierarchical learning, leads to efficient …
School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；School of Software, Dalian University of Technology（大连理工大学软件学院）；School of Telecommunications Engineering, Xidian University（西安电子科技大学电信工程学院）
Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration
Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；School of Engineering and Technology, Central Queensland University（中央昆士兰大学工程与技术学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis
Shanghai University（上海大学）；North China Electric Power University（华北电力大学）；Université de Technologie de Compiègne（法国图卢兹国立理工学院）；Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；Macquarie University（麦考瑞大学）
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Shanghai Jiao Tong University（上海交通大学）；Nanjing University（南京大学）；The Hong Kong Polytechnic University（香港理工大学）
Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. Empowering UAVs networks intelligence by artificial intelligence (AI) especially machine learning (ML) techniques is inevitable and appealing to enable the aforementioned applications. To solve the problems of traditional cloud-centric ML for UAV networks such as privacy concern, unacceptable latency, and resource burden, a distributed ML technique, \textit(i.e.), federated learning (FL), has been recently proposed to enable mu…
Université de Sherbrooke（舍布鲁克大学）；GREYC CNRS（法国国家科学研究中心灰西实验室）；ENSICAEN（法国国立卡昂高等工程师学院）；Normandie University（诺曼底大学）；University of Technology of Troyes（特鲁瓦技术大学）
Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to F…
Arizona State University（亚利桑那州立大学）；Harvard University（哈佛大学）
We consider a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data. The unreliable nature of wireless connectivity, together with constraints in computing resources at edge devices, dictates that the local updates at edge devices should be carefully crafted and compressed to match the wireless communication resources available and should work in concert with the receiver. Thus motivated, we propose SGD-based bandlimited coordinate descent algorithms for such settings. Specifically, for the wirel…
Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while keeping all the training data on device, which can not only take full advantage of data distributed across millions of nodes to train a good model but also protect data privacy. However, learning in scenario above poses new challenges. In fact, data across a massive number of unreliable devices is likely to be non-IID (identically and independently distributed), which may make the performance of models trained by federa…
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare
Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科学与技术大学）；Department of Mathematical Sciences, University of Alabama in Huntsville（数学科学系，阿拉巴马大学亨茨维尔分校）
University of Warwick（华威大学）；South China University of Technology（华南理工大学）；Shenzhen University（深圳大学）
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in th…
Google（谷歌）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；University of Edinburgh（爱丁堡大学）
Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global model. The typical clients in this setting are mobile phones, and communicatio…
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula都市数字工程中心）
Emerging technologies such as digital twins and 6th Generation mobile networks (6G) have accelerated the realization of edge intelligence in Industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users, hinder the …
The Pennsylvania State University（宾夕法尼亚州立大学）；The University of Texas at Austin（得克萨斯大学奥斯汀分校）
In federated learning, communication cost is often a critical bottleneck to scale up distributed optimization algorithms to collaboratively learn a model from millions of devices with potentially unreliable or limited communication and heterogeneous data distributions. Two notable trends to deal with the communication overhead of federated algorithms are gradient compression and local computation with periodic communication. Despite many attempts, characterizing the relationship between these two approaches has proven elusive. We address this by proposing a set of algorithms with periodical co…
Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；Institute of Industrial Science, The University of Tokyo（东京大学工业科学研究所）
We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal, this work aims to extend Federated Learning (FL), a decentralized learning framework that enables privacy-preserving training of models, to work with heterogeneous clients in a practical cellular network. The FL protocol iteratively asks random clients to download a trainable model from a server, update it with own data, and upload the update…
Enabled by the increasing availability of sensor data monitored from production machinery, condition monitoring and predictive maintenance methods are key pillars for an efficient and robust manufacturing production cycle in the Industrial Internet of Things. The employment of machine learning models to detect and predict deteriorating behavior by analyzing a variety of data collected across several industrial environments shows promising results in recent works, yet also often requires transferring the sensor data to centralized servers located in the cloud. Moreover, although collaborating a…
Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks
Department of Computer and Software Engineering, Polytechnique Montreal（计算机与软件工程系，蒙特利尔理工学院）；Department of Electrical and Computer Engineering, Concordia University（电气与计算机工程系，康科迪亚大学）
Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned
Dept. de Matemàtiques i Informàtica Universitat de Barcelona（巴塞罗那大学数学与计算机系）；Universitat de Barcelona（巴塞罗那大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究与教育研究所）
Huazhong University of Science and Technology（华中科技大学）；Xidian University（西安电子科技大学）；University of Arizona（亚利桑那大学）
With 5G cellular systems being actively deployed worldwide, the research community has started to explore novel technological advances for the subsequent generation, i.e., 6G. It is commonly believed that 6G will be built on a new vision of ubiquitous AI, an hyper-flexible architecture that brings human-like intelligence into every aspect of networking systems. Despite its great promise, there are several novel challenges expected to arise in ubiquitous AI-based 6G. Although numerous attempts have been made to apply AI to wireless networks, these attempts have not yet seen any large-scale impl…
Harbin Institute of Technology（哈尔滨工业大学）；University of Oulu（奥卢大学）
The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalized form of classic synchronous and asynchronous FL. Taking the constrained resource and unreliable nature of wireless communication into account, we jointly study the user selection and …
Peking University（北京大学）；Renmin University of China（中国人民大学）；Shanghai AI Lab（上海人工智能实验室）；Tencent Inc.（腾讯公司）；Tsinghua University（清华大学）
Federated Learning has become a widely-used framework which allows learning a global model on decentralized local datasets under the condition of protecting local data privacy. However, federated learning faces severe optimization difficulty when training samples are not independently and identically distributed (non-i.i.d.). In this paper, we point out that the client sampling practice plays a decisive role in the aforementioned optimization difficulty. We find that the negative client sampling will cause the merged data distribution of currently sampled clients heavily inconsistent with that…
