Standard Federated Learning (FL) techniques are limited to clients with identical network architectures. This restricts potential use-cases like cross-platform training or inter-organizational collaboration when both data privacy and architectural proprietary are required. We propose a new FL framework that accommodates heterogeneous client architecture by adopting a graph hypernetwork for parameter sharing. A property of the graph hyper network is that it can adapt to various computational graphs, thereby allowing meaningful parameter sharing across models. Unlike existing solutions, our framework does not limit the clients to share the same architecture type, makes no use of external data and does not require clients to disclose their model architecture. Compared with distillation-based and non-graph hypernetwork baselines, our method performs notably better on standard benchmarks. We additionally show encouraging generalization performance to unseen architectures.
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）
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…
A New Federated Learning Framework Against Gradient Inversion Attacks
The University of Hong Kong（香港大学）；Beijing University of Technology（北京工业大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；UC Santa Cruz（加州大学圣克鲁兹分校）
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；JD AI Lab（京东人工智能实验室）；Ping An Technology（平安科技）
Federated learning involves training machine learning models over devices or data silos, such as edge processors or data warehouses, while keeping the data local. Training in heterogeneous and potentially massive networks introduces bias into the system, which is originated from the non-IID data and the low participation rate in reality. In this paper, we propose Elastic Federated Learning (EFL), an unbiased algorithm to tackle the heterogeneity in the system, which makes the most informative parameters less volatile during training, and utilizes the incomplete local updates. It is an efficien…
Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for FCL
National University of Singapore（新加坡国立大学）；Southeast University（东南大学）；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）；Case Western Reserve University（凯斯西储大学）
Federated Learning is a distributed machine-learning environment that allows clients to learn collaboratively without sharing private data. This is accomplished by exchanging parameters. However, the differences in data distributions and computing resources among clients make related studies difficult. To address these heterogeneous problems, we propose a novel Federated Learning method. Our method utilizes a pre-trained model as the backbone of the local model, with fully connected layers comprising the head. The backbone extracts features for the head, and the embedding vector of classes is …
Adaptive Guidance for Local Training in Heterogeneous Federated Learning
Shanghai Jiao Tong University（上海交通大学）；Hong Kong Polytechnic University（香港理工大学）；Queen's University Belfast（贝尔法斯特女王大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Key Laboratory of Trusted Data Circulation（上海可信数据流通关键实验室）
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；International School, Beijing University of Posts and Telecommunications（北京邮电大学国际学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；State University of New York at Buffalo（纽约州立大学布法罗分校）
Huazhong University of Science and Technology（华中科技大学）；Lehigh University（理海大学）
One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for devices with different hardware resources. A scalable federated learning framework should address the heterogeneity that clients have different computing capacities and communication capabilities. To this end, this paper proposes FedHM, a novel heterogeneous federated model compression framework, distributing the heterogeneous low-rank models to clients and then aggregating them into a full-rank model. Our solution en…
Most personalised federated learning (FL) approaches assume that raw data of all clients are defined in a common subspace i.e. all clients store their data according to the same schema. For real-world applications, this assumption is restrictive as clients, having their own systems to collect and then store data, may use heterogeneous data representations. We aim at filling this gap. To this end, we propose a general framework coined FLIC that maps client's data onto a common feature space via local embedding functions. The common feature space is learnt in a federated manner using Wasserstein…
Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on heterogeneous systems due to the various computational capabilities and communication bottlenecks. In this work, we propose FedSkel to enable computation-efficient and communication-efficient federated learning on edge devices by only updating the model's essential parts, named skeleton networks. FedSkel is evaluated on real edge devices with imbalanced datasets. Experimental results show that it could achieve up to 5.5…
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…
Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL models is the co-occurrence of data heterogeneity and long-tail distribution, which frequently appears in real FL applications. In this paper, we reveal an intriguing fact that the biased classifier is the primary factor leading to the poor performance of the global model. Motivated by the above finding, we propose a novel and privacy-preserving FL method for heterogeneous and long-tailed data via Classifier Re-training with …
UESTC（电子科技大学）；Pengcheng Laboratory（鹏城实验室）；Agile and Intelligent Computing Key Laboratory of Sichuan Province（四川省敏捷与智能计算重点实验室）；Mohamed Bin Zayed University Of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Federated learning (FL) is a machine learning technique that enables participants to collaboratively train high-quality models without exchanging their private data. Participants utilizing cross-silo federated learning (CS-FL) settings are independent organizations with different task needs, and they are concerned not only with data privacy but also with independently training their unique models due to intellectual property considerations. Most existing FL methods are incapable of satisfying the above scenarios. In this study, we present a novel federated learning method CoFED based on unlabe…
We propose a novel framework to study asynchronous federated learning optimization with delays in gradient updates. Our theoretical framework extends the standard FedAvg aggregation scheme by introducing stochastic aggregation weights to represent the variability of the clients update time, due for example to heterogeneous hardware capabilities. Our formalism applies to the general federated setting where clients have heterogeneous datasets and perform at least one step of stochastic gradient descent (SGD). We demonstrate convergence for such a scheme and provide sufficient conditions for the …
We propose a novel training recipe for federated learning with heterogeneous networks where each device can have different architectures. We introduce training with a side objective to the devices of higher complexities to jointly train different architectures in a federated setting. We empirically show that our approach improves the performance of different architectures and leads to high communication savings compared to the state-of-the-art methods.
Ping An Technology (Shenzhen) Co., Ltd.（平安科技（深圳）有限公司）
Pre-trained BERT models have achieved impressive performance in many natural language processing (NLP) tasks. However, in many real-world situations, textual data are usually decentralized over many clients and unable to be uploaded to a central server due to privacy protection and regulations. Federated learning (FL) enables multiple clients collaboratively to train a global model while keeping the local data privacy. A few researches have investigated BERT in federated learning setting, but the problem of performance loss caused by heterogeneous (e.g., non-IID) data over clients remain under…
Federated learning is a promising machine learning technique that enables multiple clients to collaboratively build a model without revealing the raw data to each other. Among various types of federated learning methods, horizontal federated learning (HFL) is the best-studied category and handles homogeneous feature spaces. However, in the case of heterogeneous feature spaces, HFL uses only common features and leaves client-specific features unutilized. In this paper, we propose a HFL method using neural networks named continual horizontal federated learning (CHFL), a continual learning approa…
Personalized federated learning (FL) aims to train model(s) that can perform well for individual clients that are highly data and system heterogeneous. Most work in personalized FL, however, assumes using the same model architecture at all clients and increases the communication cost by sending/receiving models. This may not be feasible for realistic scenarios of FL. In practice, clients have highly heterogeneous system-capabilities and limited communication resources. In our work, we propose a personalized FL framework, PerFed-CKT, where clients can use heterogeneous model architectures and d…
Federated Learning (FL) is a learning paradigm that protects privacy by keeping client data on edge devices. However, optimizing FL in practice can be difficult due to the diversity and heterogeneity of the learning system. Despite recent research efforts to improve the optimization of heterogeneous data, the impact of time-evolving heterogeneous data in real-world scenarios, such as changing client data or intermittent clients joining or leaving during training, has not been studied well. In this work, we propose Continual Federated Learning (CFL), a flexible framework for capturing the time-…
Southern University of Science and Technology（南方科技大学）
Federated learning (FL) has been proposed to protect data privacy and virtually assemble the isolated data silos by cooperatively training models among organizations without breaching privacy and security. However, FL faces heterogeneity from various aspects, including data space, statistical, and system heterogeneity. For example, collaborative organizations without conflict of interest often come from different areas and have heterogeneous data from different feature spaces. Participants may also want to train heterogeneous personalized local models due to non-IID and imbalanced data distrib…
The heterogeneity of hardware and data is a well-known and studied problem in the community of Federated Learning (FL) as running under heterogeneous settings. Recently, custom-size client models trained with Knowledge Distillation (KD) has emerged as a viable strategy for tackling the heterogeneity challenge. However, previous efforts in this direction are aimed at client model tuning rather than their impact onto the knowledge aggregation of the global model. Despite performance of global models being the primary objective of FL systems, under heterogeneous settings client models have receiv…
Federated learning (FL) has been recognized as a privacy-preserving distributed machine learning paradigm that enables knowledge sharing among various heterogeneous artificial intelligence (AIoT) devices through centralized global model aggregation. FL suffers from model inaccuracy and slow convergence due to the model heterogeneity of the AIoT devices involved. Although various existing methods try to solve the bottleneck of the model heterogeneity problem, most of them improve the accuracy of heterogeneous models in a coarse-grained manner, which makes it still a great challenge to deploy la…
Duke University（杜克大学）；University of Minnesota-Twin Cities（明尼苏达大学双城分校）
Federated Learning (FL) is a method of training machine learning models on private data distributed over a large number of possibly heterogeneous clients such as mobile phones and IoT devices. In this work, we propose a new federated learning framework named HeteroFL to address heterogeneous clients equipped with very different computation and communication capabilities. Our solution can enable the training of heterogeneous local models with varying computation complexities and still produce a single global inference model. For the first time, our method challenges the underlying assumption of…
A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions
School of Electrical and Computer Engineering, National Technical University of Athens, Greece（电气与计算机工程学院，国家技术大学亚历山大学院，希腊）；Department of Informatics and Telematics, Harokopio University of Athens, Greece（信息与电信学院，哈罗基奥大学亚历山大学院，希腊）
Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；School of Systems Science and Engineering, Sun Yat-sen University（中山大学系统科学与工程学院）
