In federated learning, models trained on local clients are distilled into a global model. Due to the permutation invariance arises in neural networks, it is necessary to match the hidden neurons first when executing federated learning with neural networks. Through the Bayesian nonparametric framework, Probabilistic Federated Neural Matching (PFNM) matches and fuses local neural networks so as to adapt to varying global model size and the heterogeneity of the data. In this paper, we propose a new method which extends the PFNM with a Kullback-Leibler (KL) divergence over neural components product, in order to make inference exploiting posterior information in both local and global levels. We also show theoretically that The additional part can be seamlessly concatenated into the match-and-fuse progress. Through a series of simulations, it indicates that our new method outperforms popular state-of-the-art federated learning methods in both single communication round and additional communication rounds situation.
Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective. We present an alternative perspective and formulate federated learning as a posterior inference problem, where the goal is to infer a global posterior distribution by having client devices each infer the posterior of their local data. While exact inference is often intractable, this perspective provides a principled way to search for global optima…
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
National University of Singapore（新加坡国立大学）；Northeastern University（东北大学）；University of Technology Sydney（悉尼科技大学）；Tsinghua University（清华大学）；ETH Zurich（苏黎世联邦理工学院）
Carnegie Mellon University（卡内基梅隆大学）；Massachusetts Institute of Technology（麻省理工学院）；Columbia University（哥伦比亚大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Petuum Inc.（Petuum公司）
The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decomposes across client loss functions. A recent alternative formulation instead treats federated learning as a distributed inference problem, where the goal is to infer a global posterior from partitioned client data (Al-Shedivat et al., 2021). This paper extends the inference view and describes a variational inference formulation of federated learning where the goal is to find a global variational posterior that well-app…
University of Waterloo（滑铁卢大学）；Vector Institute（向量研究所）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
Making predictions robust is an important challenge. A separate challenge in federated learning (FL) is to reduce the number of communication rounds, particularly since doing so reduces performance in heterogeneous data settings. To tackle both issues, we take a Bayesian perspective on the problem of learning a global model. We show how the global predictive posterior can be approximated using client predictive posteriors. This is unlike other works which aggregate the local model space posteriors into the global model space posterior, and are susceptible to high approximation errors due to th…
Federated learning uses a set of techniques to efficiently distribute the training of a machine learning algorithm across several devices, who own the training data. These techniques critically rely on reducing the communication cost -- the main bottleneck -- between the devices and a central server. Federated learning algorithms usually take an optimization approach: they are algorithms for minimizing the training loss subject to communication (and other) constraints. In this work, we instead take a Bayesian approach for the training task, and propose a communication-efficient variant of the …
A Bayesian Federated Learning Framework with Online Laplace Approximation
Southern University of Science and Technology（南方科技大学）；Huazhong Agricultural University（华中农业大学）；Westlake University（西湖大学）；OPPO Research（OPPO研究院）；University of Maryland College Park（马里兰大学帕克分校）；University of Chinese Academy of Sciences（中国科学院大学）
Though successful, federated learning presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises. To cope with the statistical heterogeneity, previous works incorporated a proximal term in local optimization or modified the model aggregation scheme at the server side or advocated clustered federated learning approaches where the central server groups agent population into clusters with jointly trainable data distributions to take the advantage of a certain level of personalization. While effective, they lack a deep elaborat…
National University of Defense Technology（国防科技大学）；The University of Edinburgh（爱丁堡大学）；Samsung AI Centre（三星人工智能中心）
Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy. Mainstream FL approaches require each participant to share a common network architecture and further assume that data are are sampled IID across participants. However, in real-world deployments participants may require heterogeneous network architectures; and the data distribution is almost certainly non-uniform across participants. To address these issues we introduce FedH2L, which is agnostic to both the model architecture and robust to diffe…
Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving
College of Computer and Information Science, Southwest University, Chongqing 400715, China（西南大学计算机与信息科学学院）；School of Engineering, University of Western Australia, Australia（西澳大学工程学院）；School of Cyber Science and Technology, Beihang University, Beijing 100191, China（北航信息科学与技术学院）
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression
School of Statistics, Renmin University of China（中国人民大学统计学院）；School of Statistics and Data Science, Southeast University（东南大学统计与数据科学学院）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（未来区块链与隐私计算北京高精尖创新中心）
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Houston（休斯顿大学）；Louisiana State University（路易斯安那州立大学）；Shandong Artificial Intelligence Institute（山东省人工智能研究院）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））
Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates…
A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs
Chengdu Medical College（成都医学院）；Kunming Medical University（昆明医科大学）；National Yang Ming Chiao Tung University（国立阳明交通大学）；University of Southern California（南加州大学）；Yanshan University（燕山大学）
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
School of Statistics, Renmin University of China（中国人民大学统计学院）；Guanghua School of Management, Peking University（北京大学光华管理学院）；School of Mathematics Science, Dalian University of Technology（大连理工大学数学科学学院）；Center for Applied Statistics and School of Statistics, Renmin University of China（中国人民大学统计学院）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（北京未来区块链与隐私计算高级创新中心）
National University of Singapore（新加坡国立大学）；Roche AG（罗氏公司）；Harvard University（哈佛大学）
We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence…
Sichuan University（四川大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning. While methods utilizing sparsification or others can largely lower the communication overhead, the convergence rate is also greatly compromised. In this paper, we propose a novel method, named single-step synthetic features compressor (3SFC), to achieve communication-efficient FL by directly constructing a tiny synthetic dataset based on raw gradients. Thus, 3SFC can achieve an extremely low compression rate when the constructed dataset contains…
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients
Pusan National University（釜山国立大学）；Hanoi University of Technology（河内工业大学）；University of Sydney（悉尼大学）；Trinity College of Dublin（都柏林圣三一学院）；Zhejiang University（浙江大学）
Key Laboratory of Embedded System and Service Computing (Tongji University), Ministry of Education（嵌入式系统与服务计算教育部重点实验室（同济大学））
Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands. However, standard federated learning is vulnerable to Byzantine attacks, which will cause the global model to be manipulated by the attacker or fail to converge. On non-iid data, the current methods are not effective in defensing against Byzantine attacks. In this paper, we propose a Byzantine-robust framework for federated learning via credibility assessment on non-iid data (BRCA). Credibility assessment is designed to d…
École Polytechnique（巴黎综合理工学院）；Lagrange Mathematics and Computing Research Center（拉格朗日数学与计算研究中心）；Institut Polytechnique de Paris（巴黎理工学院）
This paper focuses on Bayesian inference in a federated learning context (FL). While several distributed MCMC algorithms have been proposed, few consider the specific limitations of FL such as communication bottlenecks and statistical heterogeneity. Recently, Federated Averaging Langevin Dynamics (FALD) was introduced, which extends the Federated Averaging algorithm to Bayesian inference. We obtain a novel tight non-asymptotic upper bound on the Wasserstein distance to the global posterior for FALD. This bound highlights the effects of statistical heterogeneity, which causes a drift in the loc…
School of Computer Science and Engineering, Nanyang Technological University, Singapore（南洋理工大学计算机科学与工程学院，新加坡）；Joint NTU-WeBank Research Centre on FinTech, NTU, Singapore（南洋理工大学-微众银行金融科技联合研究中心，新加坡南洋理工大学）
Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated learning (VFL) deals with the case where participants sharing the same sample ID space but having different feature spaces, while label information is owned by one participant. Current studies of VFL only support two participants, and mostly focus on binaryclass logistic regression problems. In this paper, we propose the Multi-participant Multi-class Vertical Federated Learning (MMVFL) framework for multi-class VFL prob…
Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
Graduate School of AI, POSTECH, Pohang, Republic of Korea（POSTECH人工智能研究生院）；Department of CSE, POSTECH, Pohang, Republic of Korea（POSTECH计算机科学与工程系）；National AI Research Lab, Seoul, Republic of Korea（首尔国家人工智能研究实验室）
The longstanding goals of federated learning (FL) require rigorous privacy guarantees and low communication overhead while holding a relatively high model accuracy. However, simultaneously achieving all the goals is extremely challenging. In this paper, we propose a novel framework called hierarchical federated learning (H-FL) to tackle this challenge. Considering the degradation of the model performance due to the statistic heterogeneity of the training data, we devise a runtime distribution reconstruction strategy, which reallocates the clients appropriately and utilizes mediators to rearran…
Sch of Info & Comm Engin, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；Sch of Comp Sci & Engin, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Sch of Comp Sci & Tech, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）
Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm. In practical FL applications, local data from each data silo reflect local usage patterns. Therefore, there exists heterogeneity of data distributions among data owners (a.k.a. FL clients). If not handled properly, this can lead to model performance degradation. This challenge has inspired the research field of heterogeneous federated learning, which currently remains open. In this paper, we propose a data heterogeneity-robust FL approach, FedGSP, to address this challenge by leveraging on …
National University of Singapore（新加坡国立大学）；MIT（麻省理工学院）
Bayesian optimization (BO) has recently been extended to the federated learning (FL) setting by the federated Thompson sampling (FTS) algorithm, which has promising applications such as federated hyperparameter tuning. However, FTS is not equipped with a rigorous privacy guarantee which is an important consideration in FL. Recent works have incorporated differential privacy (DP) into the training of deep neural networks through a general framework for adding DP to iterative algorithms. Following this general DP framework, our work here integrates DP into FTS to preserve user-level privacy. We …
Open-Source LLM-Driven Federated Transformer for Predictive IoV Management
School of Computer Science and Technology, Algoma University, Canada（计算机科学与技术学院，阿尔戈马大学，加拿大）；Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic（电气工程系，布拉格捷克技术大学，捷克共和国）
WeBank（微众银行）；Hong Kong University of Science and Technology（香港科技大学）；Shanghai Jiao Tong University（上海交通大学）
Federated learning (FL) aims to protect data privacy by cooperatively learning a model without sharing private data among users. For Federated Learning of Deep Neural Network with billions of model parameters, existing privacy-preserving solutions are unsatisfactory. Homomorphic encryption (HE) based methods provide secure privacy protections but suffer from extremely high computational and communication overheads rendering it almost useless in practice . Deep learning with Differential Privacy (DP) was implemented as a practical learning algorithm at a manageable cost in complexity. However, …
Personalized Federated Learning via Amortized Bayesian Meta-Learning
University of Electronic Science and Technology of China（电子科技大学）；Nanjing Audit University（南京审计大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng National Lab（鹏城国家实验室）
Singapore University of Technology and Design（新加坡科技设计大学）；National University of Singapore（新加坡国立大学）
Federated learning (FL) offers a solution to train a global machine learning model while still maintaining data privacy, without needing access to data stored locally at the clients. However, FL suffers performance degradation when client data distribution is non-IID, and a longer training duration to combat this degradation may not necessarily be feasible due to communication limitations. To address this challenge, we propose a new adaptive training algorithm $\texttt{AdaFL}$, which comprises two components: (i) an attention-based client selection mechanism for a fairer training scheme among …
A Secure and Private Distributed Bayesian Federated Learning Design
Beijing Laboratory of Advanced Information Network（北京先进信息网络实验室）；Beijing Key Laboratory of Network System Architecture and Convergence（北京网络系统架构与融合重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；College of Information Science and Electronic Engineering（信息科学与电子工程学院）；University of Miami（迈阿密大学）；The University of Hong Kong（香港大学）
Neural Networks (NNs) have been widely {used in supervised learning} due to their ability to model complex nonlinear patterns, often presented in high-dimensional data such as images and text. However, traditional NNs often lack the ability for uncertainty quantification. Bayesian NNs (BNNS) could help measure the uncertainty by considering the distributions of the NN model parameters. Besides, domain knowledge is commonly available and could improve the performance of BNNs if it can be appropriately incorporated. In this work, we propose a novel Posterior-Regularized Bayesian Neural Network (…
University of Oulu（奥卢大学）；Centre for Wireless Communications（无线通信中心）
The performance of federated learning (FL) over wireless networks depend on the reliability of the client-server connectivity and clients' local computation capabilities. In this article we investigate the problem of client scheduling and resource block (RB) allocation to enhance the performance of model training using FL, over a pre-defined training duration under imperfect channel state information (CSI) and limited local computing resources. First, we analytically derive the gap between the training losses of FL with clients scheduling and a centralized training method for a given training …
School of Management Engineering, Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院管理工程学院）
In clinical research, the lack of events of interest often necessitates imbalanced learning. One approach to resolve this obstacle is data integration or sharing, but due to privacy concerns neither is practical. Therefore, there is an increasing demand for a platform on which an analysis can be performed in a federated environment while maintaining privacy. However, it is quite challenging to develop a federated learning algorithm that can address both privacy-preserving and class imbalanced issues. In this study, we introduce a federated generative model learning platform for generating samp…
Fed-Credit: Robust Federated Learning with Credibility Management
Beijing Normal University at Zhuhai（北京师范大学珠海校区）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）
FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification
Media IC and System Lab, the Graduate Institute of Electronics Engineering and Department of Electrical Engineering, National Taiwan University（媒体IC与系统实验室，电子工程研究所及电气工程系，国立台湾大学）
