This paper explores previously unknown backdoor risks in HyperNet-based personalized federated learning (HyperNetFL) through poisoning attacks. Based upon that, we propose a novel model transferring attack (called HNTroj), i.e., the first of its kind, to transfer a local backdoor infected model to all legitimate and personalized local models, which are generated by the HyperNetFL model, through consistent and effective malicious local gradients computed across all compromised clients in the whole training process. As a result, HNTroj reduces the number of compromised clients needed to successfully launch the attack without any observable signs of sudden shifts or degradation regarding model utility on legitimate data samples making our attack stealthy. To defend against HNTroj, we adapted several backdoor-resistant FL training algorithms into HyperNetFL. An extensive experiment that is carried out using several benchmark datasets shows that HNTroj significantly outperforms data poisoning and model replacement attacks and bypasses robust training algorithms even with modest numbers of compromised clients.
Personalized federated learning is tasked with training machine learning models for multiple clients, each with its own data distribution. The goal is to train personalized models in a collaborative way while accounting for data disparities across clients and reducing communication costs. We propose a novel approach to this problem using hypernetworks, termed pFedHN for personalized Federated HyperNetworks. In this approach, a central hypernetwork model is trained to generate a set of models, one model for each client. This architecture provides effective parameter sharing across clients, whil…
FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworks
Tsinghua University（清华大学）；Fuyao University of Science and Technology（福耀科技大学）；Hong Kong University of Science and Technology（香港科技大学）；Shenzhen University（深圳大学）；Southern University of Science and Technology（南方科技大学）
Hypernetworks for Model-Heterogeneous Personalized Federated Learning
the Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China)（青岛软件研究所、计算机科学与技术学院、中国石油大学（华东））；the Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software（山东省智能石油与天然气工业软件重点实验室）；National University of Singapore（新加坡国立大学）
Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning
VinUni-Illinois Smart Health Center（VinUni-Illinois智能健康中心）；College of Engineering & Computer Science（工程与计算机科学学院）；University of Notre Dame（诺特丹大学）；Technische Universität Berlin（柏林技术大学）
The Pennsylvania State University（宾夕法尼亚州立大学）；NEC Laboratories America（美国NEC实验室）；Florida International University（佛罗里达国际大学）；AWS AI Labs, Amazon（亚马逊AWS人工智能实验室）
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite its popularity, it has been observed that existing PFL approaches result in sub-optimal solutions when the joint distribution among local clients diverges. To address this issue, we present Federated Modular Network (FedMN), a novel PFL approach that adaptively selects sub-modules from a module pool to assemble heterogeneous neural architectures for different clients. FedMN adopts a light-weighted routing hypernetwo…
Bar Ilan University（巴伊兰大学）；NVIDIA Research（英伟达研究院）
In Federated Learning (FL), multiple clients collaborate to learn a shared model through a central server while keeping data decentralized. Personalized Federated Learning (PFL) further extends FL by learning a personalized model per client. In both FL and PFL, all clients participate in the training process and their labeled data are used for training. However, in reality, novel clients may wish to join a prediction service after it has been deployed, obtaining predictions for their own \textbf{unlabeled} data. Here, we introduce a new learning setup, On-Demand Unlabeled PFL (OD-PFL), where a…
Robust and Personalized Federated Learning for Aircraft-Engine Prognostics under Benign and Adversarial Client Heterogeneity
Rajshahi University of Engineering & Technology（拉杰沙希工程与技术大学）；North South University（北南大学）；Missouri State University（密苏里州立大学）；Malardalen University（梅拉达伦大学）；American International University-Bangladesh（孟加拉国美国国际大学）
Multi-Agent Reinforcement Learning currently focuses on implementations where all data and training can be centralized to one machine. But what if local agents are split across multiple tasks, and need to keep data private between each? We develop the first application of Personalized Federated Hypernetworks (PFH) to Reinforcement Learning (RL). We then present a novel application of PFH to few-shot transfer, and demonstrate significant initial increases in learning. PFH has never been demonstrated beyond supervised learning benchmarks, so we apply PFH to an important domain: RL price-setting …
Recently, deep cross-modal hashing has gained increasing attention. However, in many practical cases, data are distributed and cannot be collected due to privacy concerns, which greatly reduces the cross-modal hashing performance on each client. And due to the problems of statistical heterogeneity, model heterogeneity, and forcing each client to accept the same parameters, applying federated learning to cross-modal hash learning becomes very tricky. In this paper, we propose a novel method called prototype-based layered federated cross-modal hashing. Specifically, the prototype is introduced t…
