Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization. Deep neural networks' rapid development facilitates the learning techniques for modeling complex problems and emerges into federated deep learning under the federated setting. However, the tremendous amount of model parameters burdens the communication network with a high load of transportation. This paper introduces two approaches for improving communication efficiency by dynamic sampling and top-$k$ selective masking. The former controls the fraction of selected client models dynamically, while the latter selects parameters with top-$k$ largest values of difference for federated updating. Experiments on convolutional image classification and recurrent language modeling are conducted on three public datasets to show our proposed methods' effectiveness.
University of British Columbia（不列颠哥伦比亚大学）；Purdue University（普渡大学）；Tianjin University（天津大学）
Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy. However, the substantial communication overhead of FL makes training challenging when edge devices have limited network bandwidth. Existing work to optimize FL bandwidth overlooks downstream transmission and does not account for FL client sampling. In this paper we propose GlueFL, a framework that incorporates new client sampling and model compression algorithms to mitigate low download bandwidths of FL clients. GlueFL prioritizes recently used client…
Federated Learning with Label-Masking Distillation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, UCAS（中国科学院大学网络空间安全学院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）
Model compression is important in federated learning (FL) with large models to reduce communication cost. Prior works have been focusing on sparsification based compression that could desparately affect the global model accuracy. In this work, we propose a new scheme for upstream communication where instead of transmitting the model update, each client learns and transmits a light-weight synthetic dataset such that using it as the training data, the model performs similarly well on the real training data. The server will recover the local model update via the synthetic data and apply standard …
Fudan University（复旦大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）
Today data is often scattered among billions of resource-constrained edge devices with security and privacy constraints. Federated Learning (FL) has emerged as a viable solution to learn a global model while keeping data private, but the model complexity of FL is impeded by the computation resources of edge nodes. In this work, we investigate a novel paradigm to take advantage of a powerful server model to break through model capacity in FL. By selectively learning from multiple teacher clients and itself, a server model develops in-depth knowledge and transfers its knowledge back to clients i…
Spiking Neural Networks (SNNs) are biologically inspired alternatives to conventional Artificial Neural Networks (ANNs). Despite promising preliminary results, the trade-offs in the training of SNNs in a distributed scheme are not well understood. Here, we consider SNNs in a federated learning setting where a high-quality global model is created by aggregating multiple local models from the clients without sharing any data. We investigate federated learning for training multiple SNNs at clients when two mechanisms reduce the uplink communication cost: i) random masking of the model updates sen…
SecureDrive-FL: Joint Differential Privacy and Gradient-Aware Selective Homomorphic Encryption for Federated Driver Monitoring
Institute of Industrial Automation and Software Engineering, University of Stuttgart（斯图加特大学工业自动化与软件工程研究所）；Eindhoven University of Technology (TU/e)（埃因霍温理工大学）
Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences and the University of Chinese Academy of Sciences（北京移动计算与 pervasive 设备重点实验室，计算技术研究所，中国科学院和中国科学院大学）；Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences（北京移动计算与 pervasive 设备重点实验室，计算技术研究所，中国科学院）；Shandong Academy of Intelligent Computing Technology（山东省智能计算技术研究院）；Frontier Sciences and Education, Chinese Academy of Sciences（前沿科学与教育，中国科学院）
SDBA: A Stealthy and Long-Lasting Durable Backdoor Attack in Federated Learning
Department of Information and Communication Engineering, Major in Information Security, Chosun University（信息与通信工程系，信息安全专业，全州大学）；AI Data Security Research Section, Electronics and Telecommunications Research Institute（人工智能数据安全研究部，电子电信研究院）；Department of Convergence Science, Kongju National University（融合科学系， Kongju国立大学）
Department of Computer Science, University of Warwick（华威大学计算机科学系）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Warwick Manufacturing Group (WMG), University of Warwick（华威大学华威制造集团(WMG)）；University of Warwick（华威大学）；South China University of Technology（华南理工大学）
Federated Learning (FL) has shown great potential as a privacy-preserving solution to learning from decentralized data that are only accessible to end devices (i.e., clients). In many scenarios, however, a large proportion of the clients are probably in possession of low-quality data that are biased, noisy or even irrelevant. As a result, they could significantly slow down the convergence of the global model we aim to build and also compromise its quality. In light of this, we propose FedProf, a novel algorithm for optimizing FL under such circumstances without breaching data privacy. The key …
SAU: Sparsity-Aware Unlearning for LLMs via Gradient Masking and Importance Redistribution
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；University of California San Diego（加州大学圣地亚哥分校）
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）
In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity. Traditional federated learning relies on fixed data sets, whereas in SFL, data is streamed, and its distribution changes over time, leading to discrepancies between the local training dataset and long-term distribution. To mitigate this problem, we propose three local cache update rules - First-In-First-Out (FIFO), Static Ratio Selective Replacement (SRSR), and Dynamic Ratio Selective Replacement (DRSR) - that u…
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning
A. Korkmaz Dept. of Electrical Engineering \& Computer Science, The University of Missouri, Columbia, USA P. Rao Dept. of Electrical Engineering \& Computer Science, The University of Missouri, Columbia, USA
In real-world federated learning scenarios, participants could have their own personalized labels which are incompatible with those from other clients, due to using different label permutations or tackling completely different tasks or domains. However, most existing FL approaches cannot effectively tackle such extremely heterogeneous scenarios since they often assume that (1) all participants use a synchronized set of labels, and (2) they train on the same task from the same domain. In this work, to tackle these challenges, we introduce Factorized-FL, which allows to effectively tackle label-…
TIP: Resisting Gradient Inversion via Targeted Interpretable Perturbation in Federated Learning
Shanxi Key Laboratory of Industrial Internet Security, College of Computer Science and Technology（工业互联网安全山西省重点实验室，计算机科学与技术学院）；Taiyuan University of Technology（太原科技大学）
FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technical University of Berlin（柏林工业大学）；Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；AITRICS
In practical federated learning scenarios, the participating devices may have different bitwidths for computation and memory storage by design. However, despite the progress made in device-heterogeneous federated learning scenarios, the heterogeneity in the bitwidth specifications in the hardware has been mostly overlooked. We introduce a pragmatic FL scenario with bitwidth heterogeneity across the participating devices, dubbed as Bitwidth Heterogeneous Federated Learning (BHFL). BHFL brings in a new challenge, that the aggregation of model parameters with different bitwidths could result in s…
The Federated Learning setting has a central server coordinating the training of a model on a network of devices. One of the challenges is variable training performance when the dataset has a class imbalance. In this paper, we address this by introducing a new loss function called Fed-Focal Loss. We propose to address the class imbalance by reshaping cross-entropy loss such that it down-weights the loss assigned to well-classified examples along the lines of focal loss. Additionally, by leveraging a tunable sampling framework, we take into account selective client model contributions on the ce…
Masked Random Noise for Communication Efficient Federated Learning
Huazhong University of Science and Technology（华中科技大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；FiT, Tencent（腾讯FiT）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
Federated learning (FL) aims to collaboratively train the global model in a distributed manner by sharing the model parameters from local clients to a central server, thereby potentially protecting users' private information. Nevertheless, recent studies have illustrated that FL still suffers from information leakage as adversaries try to recover the training data by analyzing shared parameters from local clients. To deal with this issue, differential privacy (DP) is adopted to add noise to the gradients of local models before aggregation. It, however, results in the poor performance of gradie…
Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Lab（量子云计算与分布式系统实验室）；School of Computing and Information Systems（计算与信息系统学院）；The University of Melbourne, Australia（墨尔本大学）
Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks
Institute of Information & Communications Technology Planning & Evaluation (IITP)-ITRC (Information Technology Research Center)（信息与通信技术规划与评估院（IITP）-ITRC（信息技术研究中心））
