In this paper, we identify a new phenomenon called activation-divergence which occurs in Federated Learning (FL) due to data heterogeneity (i.e., data being non-IID) across multiple users. Specifically, we argue that the activation vectors in FL can diverge, even if subsets of users share a few common classes with data residing on different devices. To address the activation-divergence issue, we introduce a prior based on the principle of maximum entropy; this prior assumes minimal information about the per-device activation vectors and aims at making the activation vectors of same classes as similar as possible across multiple devices. Our results show that, for both IID and non-IID settings, our proposed approach results in better accuracy (due to the significantly more similar activation vectors across multiple devices), and is more communication-efficient than state-of-the-art approaches in FL. Finally, we illustrate the effectiveness of our approach on a few common benchmarks and two large medical datasets.
As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored. FL is a rising approach for collaborative ML, in which an aggregator orchestrates multiple parties to train a global model without sharing their training data. In this paper, we discuss causes of bias in FL and propose three pre-processing and in-processing methods to mitigate bias, without compromising data privacy, a key FL requirement. As data heterogeneity among parties is one of the challenging characteristics of FL, we conduct experiments over several data d…
Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；University of Waterloo（滑铁卢大学）
Federated Learning (FL) is a prominent framework that enables training a centralized model while securing user privacy by fusing local, decentralized models. In this setting, one major obstacle is data heterogeneity, i.e., each client having non-identically and independently distributed (non-IID) data. This is analogous to the context of Domain Generalization (DG), where each client can be treated as a different domain. However, while many approaches in DG tackle data heterogeneity from the algorithmic perspective, recent evidence suggests that data augmentation can induce equal or greater per…
Princeton University（普林斯顿大学）；University of Chicago（芝加哥大学）；IBM
Federated learning is inherently vulnerable to model poisoning attacks because its decentralized nature allows attackers to participate with compromised devices. In model poisoning attacks, the attacker reduces the model's performance on targeted sub-tasks (e.g. classifying planes as birds) by uploading "poisoned" updates. In this report we introduce \algoname{}, a novel defense that uses global top-k update sparsification and device-level gradient clipping to mitigate model poisoning attacks. We propose a theoretical framework for analyzing the robustness of defenses against poisoning attacks…
Hong Kong University of Science & Technology（香港科技大学）
In federated learning, multiple parties collaborate in order to train a global model over their respective datasets. Even though cryptographic primitives (e.g., homomorphic encryption) can help achieve data privacy in this setting, some partial information may still be leaked across parties if this is done non-judiciously. In this work, we study the federated learning framework of SecureBoost [Cheng et al., FL@IJCAI'19] as a specific such example, demonstrate a leakage-abuse attack based on its leakage profile, and experimentally evaluate the effectiveness of our attack. We then propose two se…
Machine learning (ML) over distributed multi-party data is required for a variety of domains. Existing approaches, such as federated learning, collect the outputs computed by a group of devices at a central aggregator and run iterative algorithms to train a globally shared model. Unfortunately, such approaches are susceptible to a variety of attacks, including model poisoning, which is made substantially worse in the presence of sybils. In this paper we first evaluate the vulnerability of federated learning to sybil-based poisoning attacks. We then describe \emph{FoolsGold}, a novel defense to…
Pennsylvania State University（宾夕法尼亚州立大学）；North Carolina State University（北卡罗来纳州立大学）
Malicious clients can attack federated learning systems using malicious data, including backdoor samples, during the training phase. The compromised global model will perform well on the validation dataset designed for the task, but a small subset of data with backdoor patterns may trigger the model to make a wrong prediction. There has been an arms race between attackers who tried to conceal attacks and defenders who tried to detect attacks during the aggregation stage of training on the server-side. In this work, we propose a new and effective method to mitigate backdoor attacks after the tr…
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）
The paradigm of Federated learning (FL) deals with multiple clients participating in collaborative training of a machine learning model under the orchestration of a central server. In this setup, each client's data is private to itself and is not transferable to other clients or the server. Though FL paradigm has received significant interest recently from the research community, the problem of selecting the relevant clients w.r.t. the central server's learning objective is under-explored. We refer to these problems as Federated Relevant Client Selection (FRCS). Because the server doesn't have…
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…
Federated Learning (FL) allows multiple clients to collaboratively train a Neural Network (NN) model on their private data without revealing the data. Recently, several targeted poisoning attacks against FL have been introduced. These attacks inject a backdoor into the resulting model that allows adversary-controlled inputs to be misclassified. Existing countermeasures against backdoor attacks are inefficient and often merely aim to exclude deviating models from the aggregation. However, this approach also removes benign models of clients with deviating data distributions, causing the aggregat…
University of Maryland, College Park（马里兰大学帕克分校）；IBM Research（IBM研究院）
Federated learning has arisen as a mechanism to allow multiple participants to collaboratively train a model without sharing their data. In these settings, participants (workers) may not trust each other fully; for instance, a set of competitors may collaboratively train a machine learning model to detect fraud. The workers provide local gradients that a central server uses to update a global model. This global model can be corrupted when Byzantine workers send malicious gradients, which necessitates robust methods for aggregating gradients that mitigate the adverse effects of Byzantine inputs…
Purdue University（普渡大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. Building upon recent foundations in federated learning, in our approach, personalized models for individual student subgroups are derived from a global model aggregated across all student models via meta-gradient updates that account for subgroup …
Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes
Department of Computer Science and Engineering, School of Computing, Kyung Hee University（计算机科学与工程系，计算学院，庆熙大学）；Department of Artificial Intelligence, School of Computing, Kyung Hee University（人工智能系，计算学院，庆熙大学）；Digital Science and Technology Institute, The University of Danang—Vietnam-Korea University of Information and Communication Technology（数字科学与技术研究所，丹那大学—越南-韩国信息与通信技术大学）
Technische Universität München（慕尼黑工业大学）；Huawei Technologies（华为技术有限公司）
Federated Learning (FL) is a machine learning paradigm that enables the training of a shared global model across distributed clients while keeping the training data local. While most prior work on designing systems for FL has focused on using stateful always running components, recent work has shown that components in an FL system can greatly benefit from the usage of serverless computing and Function-as-a-Service technologies. To this end, distributed training of models with serverless FL systems can be more resource-efficient and cheaper than conventional FL systems. However, serverless FL s…
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
Queen Mary University of London（伦敦玛丽女王大学）；University of Oxford（牛津大学）；Huazhong University of Science and Technology（华中科技大学）；Shandong University of Technology（山东理工大学）
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
MetaMind Innovation P.C.（MetaMind创新有限公司）；University of Western Macedonia（西马其顿大学）；K3Y Ltd（K3Y有限公司）；Democritus University of Thrace（色雷斯德谟克利特大学）；Kingston University London（伦敦金斯顿大学）
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
Department of ECE and INMC, Seoul National University, Seoul, South Korea（首尔国立大学电子与信息科学系及网络信息中心，首尔，韩国）；Graduate School of Data Science, Seoul National University, Seoul, South Korea（首尔国立大学数据科学研究生院，首尔，韩国）；Department of Computer Science, University of Colorado Boulder, CO, USA（科罗拉多大学博尔德分校计算机科学系，科罗拉多州，美国）
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing
Norwegian University of Science and Technology（挪威科技大学）；Aalto University（阿尔托大学）；Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）；University of Southern Denmark（南丹麦大学）
Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory
Polytechnique Montréal（蒙特利尔综合理工学院）；Institut national de la recherche scientifique (INRS-EMT)（加拿大国家科学研究院（INRS-EMT））；Khalifa University（哈利法大学）；Lebanese American University（黎巴嫩美国大学）
FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；National Engineering Research Center of Disaster Backup and Recovery, Beijing University of Posts and Telecommunications（北京邮电大学国家灾备工程技术研究中心）；JIUTIAN Research, China Mobile（中国移动九天研究院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）；University of Leicester（莱斯特大学）
