Non-Independent and Identically Distributed (non- IID) data distribution among clients is considered as the key factor that degrades the performance of federated learning (FL). Several approaches to handle non-IID data such as personalized FL and federated multi-task learning (FMTL) are of great interest to research communities. In this work, first, we formulate the FMTL problem using Laplacian regularization to explicitly leverage the relationships among the models of clients for multi-task learning. Then, we introduce a new view of the FMTL problem, which in the first time shows that the formulated FMTL problem can be used for conventional FL and personalized FL. We also propose two algorithms FedU and dFedU to solve the formulated FMTL problem in communication-centralized and decentralized schemes, respectively. Theoretically, we prove that the convergence rates of both algorithms achieve linear speedup for strongly convex and sublinear speedup of order 1/2 for nonconvex objectives. Experimentally, we show that our algorithms outperform the algorithm FedAvg, FedProx, SCAFFOLD, and AFL in FL settings, MOCHA in FMTL settings, as well as pFedMe and Per-FedAvg in personalized FL settings.
In this paper, we study communication efficient distributed algorithms for distributionally robust federated learning via periodic averaging with adaptive sampling. In contrast to standard empirical risk minimization, due to the minimax structure of the underlying optimization problem, a key difficulty arises from the fact that the global parameter that controls the mixture of local losses can only be updated infrequently on the global stage. To compensate for this, we propose a Distributionally Robust Federated Averaging (DRFA) algorithm that employs a novel snapshotting scheme to approximate…
Boston University（波士顿大学）；Arm ML Research Lab（Arm机器学习研究实验室）
We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round. We view Federated Learning problem primarily from a communication perspective and allow more device level computations to save transmission costs. We point out a fundamental dilemma, in that the minima of the local-device level empirical loss are inconsistent with those of the global empirical loss. Different from recent prior works, that either attempt inexact minimization or utilize devices for par…
Hong Kong University of Science and Technology（香港科技大学）；University of Utah（犹他大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. However, an adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models. In this paper, we investigate a utility enhancement scheme based on Laplacian smoothing for differentially private federated learning (DP-Fed-LS), where the parameter aggregation with injected Gaussian n…
A critical challenge of federated learning is data heterogeneity and imbalance across clients, which leads to inconsistency between local networks and unstable convergence of global models. To alleviate the limitations, we propose a novel architectural regularization technique that constructs multiple auxiliary branches in each local model by grafting local and global subnetworks at several different levels and that learns the representations of the main pathway in the local model congruent to the auxiliary hybrid pathways via online knowledge distillation. The proposed technique is effective …
Institute for AI Industry Research, Tsinghua University（清华大学智能产业研究院）
Vertical Federated Learning (VFL) is widely utilized in real-world applications to enable collaborative learning while protecting data privacy and safety. However, previous works show that parties without labels (passive parties) in VFL can infer the sensitive label information owned by the party with labels (active party) or execute backdoor attacks to VFL. Meanwhile, active party can also infer sensitive feature information from passive party. All these pose new privacy and security challenges to VFL systems. We propose a new general defense method which limits the mutual information between…
We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms. The principal advantage of this class of methods is to better accommodate the real-world characteristics found in federated training, such as the lack of IID data across parties, the need for robustness to outliers or stragglers, and the requirement to perform well on party-specific datasets. We achieve this through a problem formulation that allows the central server to employ robust ways of aggregating the local models while keeping the structure of local c…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
User-level differential privacy (DP) provides certifiable privacy guarantees to the information that is specific to any user's data in federated learning. Existing methods that ensure user-level DP come at the cost of severe accuracy decrease. In this paper, we study the cause of model performance degradation in federated learning under user-level DP guarantee. We find the key to solving this issue is to naturally restrict the norm of local updates before executing operations that guarantee DP. To this end, we propose two techniques, Bounded Local Update Regularization and Local Update Sparsif…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, since high-quality labeled data require expensive human intelligence and efforts, data with incorrect labels (called noisy labels) are ubiquitous in reality, which inevitably cause performance degradation. Although a lot of methods are proposed to directly deal with noisy labels, these methods either require excessive computation overhead or violate the privacy protection principle of FL. To this end, we focus on this issue in FL with the …
As annotations of data can be scarce in large-scale practical problems, leveraging unlabelled examples is one of the most important aspects of machine learning. This is the aim of semi-supervised learning. To benefit from the access to unlabelled data, it is natural to diffuse smoothly knowledge of labelled data to unlabelled one. This induces to the use of Laplacian regularization. Yet, current implementations of Laplacian regularization suffer from several drawbacks, notably the well-known curse of dimensionality. In this paper, we provide a statistical analysis to overcome those issues, and…
Federated learning is widely used to perform decentralized training of a global model on multiple devices while preserving the data privacy of each device. However, it suffers from heterogeneous local data on each training device which increases the difficulty to reach the same level of accuracy as the centralized training. Supervised Contrastive Learning which outperform cross-entropy tries to minimizes the difference between feature space of points belongs to the same class and pushes away points from different classes. We propose Supervised Contrastive Federated Learning in which devices ca…
A major obstacle to achieving global convergence in distributed and federated learning is the misalignment of gradients across clients, or mini-batches due to heterogeneity and stochasticity of the distributed data. In this work, we show that data heterogeneity can in fact be exploited to improve generalization performance through implicit regularization. One way to alleviate the effects of heterogeneity is to encourage the alignment of gradients across different clients throughout training. Our analysis reveals that this goal can be accomplished by utilizing the right optimization method that…
The graph Laplacian regularization term is usually used in semi-supervised representation learning to provide graph structure information for a model $f(X)$. However, with the recent popularity of graph neural networks (GNNs), directly encoding graph structure $A$ into a model, i.e., $f(A, X)$, has become the more common approach. While we show that graph Laplacian regularization brings little-to-no benefit to existing GNNs, and propose a simple but non-trivial variant of graph Laplacian regularization, called Propagation-regularization (P-reg), to boost the performance of existing GNN models.…
Federated Distributional Reinforcement Learning with Distributional Critic Regularization
Dept. of Mechanical Engineering, Rochester Institute of Technology（机械工程系，罗切斯特理工学院）；Dept. of Psychology and School of Information, Rochester Institute of Technology（心理学系和信息学院，罗切斯特理工学院）；Department of Engineering Science, University West（工程科学系，西大学）；Golisano College of Computing and Information Sciences, Rochester Institute of Technology（计算与信息科学学院，罗切斯特理工学院）
FAIRVAR: Fair Federated Learning via Variance Regularization
Department of Computing Science, Umeå University, Umeå, Sweden（计算机科学系，乌梅大学，乌梅，瑞典）；Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden（数学与统计学系，乌梅大学，乌梅，瑞典）
FedDSR: Federated Deep Supervision and Regularization Towards Autonomous Driving
Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）；Department of Computer Science, City University of Hong Kong（城市大学计算机科学系）；Shenzhen International Center For Industrial And Applied Mathematics, Shenzhen Research Institute of Big Data（深圳国际工业与应用数学中心、深圳大数据研究院）
Sparse Federated Echo State Networks for Time-Series Classification via Smooth-L1 Regularization
Faculty of Computer Science and Business Information Systems（计算机科学与商业信息系统学院）；Technical University of Applied Sciences Würzburg-Schweinfurt（维尔茨堡-施韦因富特应用技术大学）
FedNAR: Federated Optimization with Normalized Annealing Regularization
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Maryland（马里兰大学）；Carnegie Mellon University（卡内基梅隆大学）；Petuum, Inc（Petuum公司）
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
University of Helsinki（赫尔辛基大学）；University of Technology Sydney（悉尼科技大学）；Beihang University（北京航空航天大学）；Monash University（莫纳什大学）；Aalto University（阿尔托大学）；Griffith University（格里菲斯大学）；Amazon（亚马逊公司）；Columbia University（哥伦比亚大学）
Indian Institute of Space Science and Technology（印度空间科学与技术研究所）
Graph convolutional neural networks (GCNNs) have been widely used in graph learning. It has been observed that the smoothness functional on graphs can be defined in terms of the graph Laplacian. This fact points out in the direction of using Laplacian in deriving regularization operators on graphs and its consequent use with spectral GCNN filter designs. In this work, we explore the regularization properties of graph Laplacian and proposed a generalized framework for regularized filter designs in spectral GCNNs. We found that the filters used in many state-of-the-art GCNNs can be derived as a …
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
The University of British Columbia（英属哥伦比亚大学）；Fudan University（复旦大学）；Royal College of Science, Imperial College London（伦敦帝国理工学院皇家科学学院）；Dyson School of Design Engineering（戴森设计工程学院）；Suzhou Institute of Biomedical Engineering and Technology (SIBET), Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；East China Normal University（华东师范大学）
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization
Hanoi University of Science and Technology（河内科学技术大学）；Nagoya University（名古屋大学）；Washington State University（华盛顿州立大学）；Swiss Federal Institute of Technology Lausanne（洛桑联邦理工学院）
Federated Learning (FL) seeks to distribute model training across local clients without collecting data in a centralized data-center, hence removing data-privacy concerns. A major challenge for FL is data heterogeneity (where each client's data distribution can differ) as it can lead to weight divergence among local clients and slow global convergence. The current SOTA FL methods designed for data heterogeneity typically impose regularization to limit the impact of non-IID data and are stateful algorithms, i.e., they maintain local statistics over time. While effective, these approaches can on…
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization
Department of Computer Science and Engineering, University of Notre Dame（Notre Dame 大学计算机科学与工程系）；College of Engineering and Computer Science, VinUniversity（Vin大学工程与计算机科学学院）；Open Distributed Systems, Technical University Berlin（柏林技术大学开放分布式系统）
Sookmyung Women’s University（淑明女子大学）；UNIST（蔚山科学技术院）；University of California Berkeley（加州大学伯克利分校）；Seoul National University（首尔国立大学）
Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging. Several works have addressed the problem by investigating semi-parametric contextual bandits and warned that ignoring non-stationarity could harm performances. Another prevalent human behavior is social interaction which has become available in a form of a social network or graph structure. As a result, graph-based contextual bandits have received much attention. In this paper, we propose "SemiGraphTS," a novel contextual Thompson-sampling algorithm for a graph-based semi-parametric rew…
OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（清华大学深圳国际研究生院，清华大学，深圳，中国）；Shenzhen Research Institute of Big Data, Shenzhen, China（深圳大数据研究院，深圳，中国）；Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China（南方科技大学计算机科学与工程系，深圳，中国）；Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China（香港大学电子与电气工程系，香港，中国）；Yinwang Intelligent Technology Co. Ltd., Shenzhen, China（云网智能科技有限公司，深圳，中国）
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Department of Computer Science and Engineering, University at Buffalo, the State University of New York（纽约州立大学布法罗分校计算机科学与工程系）
