In the last years, Federated learning (FL) has become a popular solution to train machine learning models in domains with high privacy concerns. However, FL scalability and performance face significant challenges in real-world deployments where data across devices are non-independently and identically distributed (non-IID). The heterogeneity in data distribution frequently arises from spatial distribution of devices, leading to degraded model performance in the absence of proper handling. Additionally, FL typical reliance on centralized architectures introduces bottlenecks and single-point-of-failure risks, particularly problematic at scale or in dynamic environments. To close this gap, we propose Field-Based Federated Learning (FBFL), a novel approach leveraging macroprogramming and field coordination to address these limitations through: (i) distributed spatial-based leader election for personalization to mitigate non-IID data challenges; and (ii) construction of a self-organizing, hierarchical architecture using advanced macroprogramming patterns. Moreover, FBFL not only overcomes the aforementioned limitations, but also enables the development of more specialized models tailored to the specific data distribution in each subregion. This paper formalizes FBFL and evaluates it extensively using MNIST, FashionMNIST, and Extended MNIST datasets. We demonstrate that, when operating under IID data conditions, FBFL performs comparably to the widely-used FedAvg algorithm. Furthermore, in challenging non-IID scenarios, FBFL not only outperforms FedAvg but also surpasses other state-of-the-art methods, namely FedProx and Scaffold, which have been specifically designed to address non-IID data distributions. Additionally, we showcase the resilience of FBFL's self-organizing hierarchical architecture against server failures.
Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity
Institute of Informatics and Telematics (IIT) of the Italian National Research Council (CNR)（意大利国家研究委员会（CNR）信息学与电信研究所（IIT））；Central European University (CEU)（中欧大学（CEU））；Rényi Institute of Mathematics（雷尼数学研究所）；Tampere University (TAU)（坦佩雷大学（TAU））
Singapore University of Technology and Design（新加坡科技设计大学）；ZJU-UIUC Institute, Zhejiang University（浙江大学浙江大学-伊利诺伊大学厄巴纳香槟校区联合学院）
Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. But the training efficiency is often throttled by challenges arising from limited communication and data heterogeneity. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck. Additionally, we leverage a bi-level optimization framework to personalize the…
On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach
School of Computer Technologies, RMIT University（RMIT大学计算机技术学院）；School of Science, Computing and Engineering Technologies, Swinburne University of Technology（斯威丁大学科学与工程技术学院）
In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure privacy. Normally, inconsistent distribution of data across different devices in a federated network and limited communication bandwidth between end devices impose both statistical heterogeneity and expensive communication as major challenges for federated learning. This paper proposes an algorithm to achieve more fairness and accuracy in federated learning (FedFa). It introduces an optimization scheme that employs a dou…
School of Software, Shandong University, Jinan, China（软件学院，山东大学，济南，中国）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, China（人工智能研究联合中心（C-FAIR），山东大学，中国）；College of Computing and Data Science, Nanyang Technological University, Singapore（计算与数据科学学院，南洋理工大学，新加坡）
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. However, solving federated machine learning problems raises issues above and beyond those of centralized machine learning. These issues include setting up communication infrastructure between parties, coordinating the learning process, integrating party results, understanding the characteristics of the training data sets of different participating parties, handling data heterogeneity, and operating with the absence of a…
Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR
University at Buffalo–SUNY（布法罗大学-纽约州立大学）；Department of Electrical Engineering（电气工程系）；New Jersey Institute of Technology (NJIT)（新泽西理工学院）
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）
MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites
Gannan University of Science and Technology（赣南科技学院）；Tsinghua University（清华大学）；Queen University（女王大学）；Lingnan University（岭南大学）；Central South University（中南大学）
