Fairness and robustness are two important concerns for federated learning systems. In this work, we identify that robustness to data and model poisoning attacks and fairness, measured as the uniformity of performance across devices, are competing constraints in statistically heterogeneous networks. To address these constraints, we propose employing a simple, general framework for personalized federated learning, Ditto, that can inherently provide fairness and robustness benefits, and develop a scalable solver for it. Theoretically, we analyze the ability of Ditto to achieve fairness and robustness simultaneously on a class of linear problems. Empirically, across a suite of federated datasets, we show that Ditto not only achieves competitive performance relative to recent personalization methods, but also enables more accurate, robust, and fair models relative to state-of-the-art fair or robust baselines.
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning
Beijing University of Posts and Telecommunications（北京邮电大学）；Macquarie University（麦考瑞大学）；Peng Cheng Laboratory（鹏城实验室）；University of Technology Sydney（悉尼大学）；University of Manitoba（曼尼托巴大学）
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computer and Information Engineering, Jiangxi Normal University（江西师范大学计算机与信息工程学院）；La Trobe Business School, La Trobe University（拉筹伯大学拉筹伯商学院）；School of Computing Technologies, RMIT University（皇家墨尔本理工大学计算技术学院）；Hong Kong University of Science and Technology（香港科技大学）
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）
Google Research（谷歌研究院）；Carnegie Mellon University（卡内基梅隆大学）
Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been purported to improve metrics such as accuracy, fairness, and robustness in federated networks. However, despite a plethora of work in this area, it remains unclear: (1) which personalization techniques are most effective in various settings, and (2) how important personalization truly is for realistic federated applications. To better answer these questions, we propose Motley, a benchmark for personalized federated learning. Motley consists …
Federated learning allows clients to collaboratively learn statistical models while keeping their data local. Federated learning was originally used to train a unique global model to be served to all clients, but this approach might be sub-optimal when clients' local data distributions are heterogeneous. In order to tackle this limitation, recent personalized federated learning methods train a separate model for each client while still leveraging the knowledge available at other clients. In this work, we exploit the ability of deep neural networks to extract high quality vectorial representati…
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, …
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 …
Federated learning (FL) is a decentralized privacy-preserving learning technique in which clients learn a joint collaborative model through a central aggregator without sharing their data. In this setting, all clients learn a single common predictor (FedAvg), which does not generalize well on each client's local data due to the statistical data heterogeneity among clients. In this paper, we address this problem with PersFL, a discrete two-stage personalized learning algorithm. In the first stage, PersFL finds the optimal teacher model of each client during the FL training phase. In the second …
University of Helsinki（赫尔辛基大学）；Tongji University（同济大学）；Hong Kong University of Science and Technology（香港科技大学）
Federated learning has attracted attention in recent years for collaboratively training data on distributed devices with privacy-preservation. The limited network capacity of mobile and IoT devices has been seen as one of the major challenges for cross-device federated learning. Recent solutions have been focusing on threshold-based client selection schemes to guarantee the communication efficiency. However, we find this approach can cause biased client selection and results in deteriorated performance. Moreover, we find that the challenge of network limit may be overstated in some cases and t…
We propose an asymptotic framework to analyze the performance of (personalized) federated learning algorithms. In this new framework, we formulate federated learning as a multi-criterion objective, where the goal is to minimize each client's loss using information from all of the clients. We analyze a linear regression model where, for a given client, we may theoretically compare the performance of various algorithms in the high-dimensional asymptotic limit. This asymptotic multi-criterion approach naturally models the high-dimensional, many-device nature of federated learning. These tools mak…
Self-reinforcing feedback loops are both cause and effect of over and/or under-presentation of some content in interactive recommender systems. This leads to erroneous user preference estimates, namely, overestimation of over-presented content while violating the right to be presented of each alternative, contrary of which we define as a fair system. We consider two models that explicitly incorporate, or ignore the systematic and limited exposure to alternatives. By simulations, we demonstrate that ignoring the systematic presentations overestimates promoted options and underestimates censored…
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（信息与通信工程学院，北京邮电大学）；School of Computing, Macquarie University（计算学院，麦考瑞大学）；Department of Broadband Communication, Peng Cheng Laboratory（宽带通信部，鹏城实验室）；China Telecom, Sichuan Branch（中国电信四川分公司）
Indian Institute of Technology, Bhilai（印度理工学院比莱分校）
The traditional framework of federated learning (FL) requires each client to re-train their models in every iteration, making it infeasible for resource-constrained mobile devices to train deep-learning (DL) models. Split learning (SL) provides an alternative by using a centralized server to offload the computation of activations and gradients for a subset of the model but suffers from problems of slow convergence and lower accuracy. In this paper, we implement PFSL, a new framework of distributed split learning where a large number of thin clients perform transfer learning in parallel, starti…
Decision making in crucial applications such as lending, hiring, and college admissions has witnessed increasing use of algorithmic models and techniques as a result of a confluence of factors such as ubiquitous connectivity, ability to collect, aggregate, and process large amounts of fine-grained data using cloud computing, and ease of access to applying sophisticated machine learning models. Quite often, such applications are powered by search and recommendation systems, which in turn make use of personalized ranking algorithms. At the same time, there is increasing awareness about the ethic…
\'Ecole Polytechnique F\'ed\'erale de Lausanne (EPFL)
Personalization is pervasive in the online space as, when combined with learning, it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user. However, recent studies suggest that such personalization can propagate societal or systemic biases, which has led to calls for regulatory mechanisms and algorithms to combat inequality. Here we propose a rigorous algorithmic framework that allows for the possibility to control biased or discriminatory personalization with respect to sensitive attributes of users without losing all of the benefits of persona…
Carnegie Mellon University（卡内基梅隆大学）；Nokia Bell Labs（诺基亚贝尔实验室）
Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. While we witness increasing applications of FL in the area of mobile sensing, such as human activity recognition (HAR), FL has not been studied in the context of a multi-device environment (MDE), wherein each user owns multiple data-producing devices. With the proliferation of mobile and wearable devices, MDEs are increasingly becoming popular in ubicomp settings, therefore necessitating the study of FL in them. FL in MDEs is characterized by being not independen…
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning
School of Electrical and Computer Engineering, The University of Sydney（悉尼大学电气与计算机工程学院）；Department of Information Systems and Cyber Security, University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校信息系统与网络安全系）
Scuola Normale Superiore（比萨高等师范学校）；INRIA（法国国家信息与自动化研究所）；École Polytechnique（巴黎综合理工学院）；Scuola Superiore Sant’Anna（圣安娜高等学校）
Federated learning (FL) is a type of collaborative machine learning where participating peers/clients process their data locally, sharing only updates to the collaborative model. This enables to build privacy-aware distributed machine learning models, among others. The goal is the optimization of a statistical model's parameters by minimizing a cost function of a collection of datasets which are stored locally by a set of clients. This process exposes the clients to two issues: leakage of private information and lack of personalization of the model. On the other hand, with the recent advanceme…
From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse
Saarland Informatics Campus, Saarland University（萨尔大学萨尔兰信息学园区）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）；Institute for Psychology, University of Freiburg（弗莱堡大学心理学研究所）
Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（安全、可靠与信任跨学科研究中心（SnT），卢森堡大学）；Blekinge Institute of Technology（布莱金厄理工大学）；ADSLabs, Umea University（ADSLabs，乌梅亚大学）
