The rapid development of Large Language Models (LLMs) has been pivotal in advancing AI, with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning. Federated learning (FL) further enhances fine-tuning in a privacy-aware manner by utilizing clients' local data through in-situ computation, eliminating the need for data movement. However, fine-tuning LLMs, given their massive scale of parameters, poses challenges for clients with constrained and heterogeneous resources in FL. Previous methods employed low-rank adaptation (LoRA) for efficient federated fine-tuning but utilized traditional FL aggregation strategies on LoRA adapters. These approaches led to mathematically inaccurate aggregation noise, reducing fine-tuning effectiveness and failing to address heterogeneous LoRAs. In this work, we first highlight the mathematical incorrectness of LoRA aggregation in existing federated fine-tuning methods. We introduce a new approach called FLORA that enables federated fine-tuning on heterogeneous LoRA adapters across clients through a novel stacking-based aggregation method. Our approach is noise-free and seamlessly supports heterogeneous LoRA adapters. Extensive experiments demonstrate FLORA' s superior performance in both homogeneous and heterogeneous settings, surpassing state-of-the-art methods. We envision this work as a milestone for efficient, privacy-preserving, and accurate federated fine-tuning of LLMs. Our code is available at https://github.com/ATP-1010/FederatedLLM.
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss suRface Aggregation (FLoRA), the first FL-HPO solution framework that can address use cases of tabular data and gradient boosting training algorithms in addition to stochastic gradient descent/neural networks commonly addressed in the FL literature. The framework enables single-shot FL-HPO, by first identifying a good set of hyper-parameters that are used in a **single** FL training. Thus, it enables FL-HPO solutions with minimal additional communicati…
In this paper, a new learning algorithm for Federated Learning (FL) is introduced. The proposed scheme is based on a weighted gradient aggregation using two-step optimization to offer a flexible training pipeline. Herein, two different flavors of the aggregation method are presented, leading to an order of magnitude improvement in convergence speed compared to other distributed or FL training algorithms like BMUF and FedAvg. Further, the aggregation algorithm acts as a regularizer of the gradient quality. We investigate the effect of our FL algorithm in supervised and unsupervised Speech Recog…
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a general FL-HPO solution framework that can address use cases of tabular data and any Machine Learning (ML) model including gradient boosting training algorithms and therefore further expands the scope of FL-HPO. FLoRA enables single-shot FL-HPO: identifying a single set of good hyper-parameters that are subsequently used in a single FL training. Thus, it enables FL-HPO solutions with minimal additional communication overh…
University at Buffalo（布法罗大学）；Ant Group（蚂蚁集团）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of the classes of labels required to train a global model. To tackle this problem, we propose a distributed minimax optimizer referred to as FedMM, designed specifically for the federated adversary domain adaptation problem. It works well even in the extreme case where each client has different label classes and some clients only have unsupervised tasks. We prove that FedMM ensures convergence to a stationary point wit…
Universidade da Coruña（拉科鲁尼亚大学）；Universidade de Santiago de Compostela（圣地亚哥德孔波斯特拉大学）
Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the user experience. Federated learning is a young and popular framework that allows multiple distributed devices to train deep learning models collaboratively while preserving data privacy. Nevertheless, this approach may not be optimal for scenarios where data distribution is non-identical among the participants or changes over time, causing what is…
Michigan Technological University（密歇根理工大学）；University of Hong Kong（香港大学）
Given the rapidly changing machine learning environments and expensive data labeling, semi-supervised domain adaptation (SSDA) is imperative when the labeled data from the source domain is statistically different from the partially labeled data from the target domain. Most prior SSDA research is centrally performed, requiring access to both source and target data. However, data in many fields nowadays is generated by distributed end devices. Due to privacy concerns, the data might be locally stored and cannot be shared, resulting in the ineffectiveness of existing SSDA research. This paper pro…
pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data
Center for Computational & Data Sciences (CCDS)（计算与数据科学中心）；Independent University（独立大学）；University of Central Florida（中央佛罗里达大学）；Curtin University（Curtin大学）；Umeå University（乌梅拉大学）
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning
1 Center for Computational \& Data Sciences, Independent University, Bangladesh；2 Curtin University；Center for Computational \& Data Sciences Independent University, Bangladesh；School of Electrical Engineering, Computing \& Mathematical Sciences (EECMS) Curtin University
Selective Aggregation for Low-Rank Adaptation in Federated Learning
The University of Hong Kong（香港大学）；Stanford University（斯坦福大学）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Materials Innovation Institute for Life Sciences and Energy (MILES), HKU-SIRI（香港大学-SIRI生命科学与能源材料创新研究所）
Federated LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts
North Carolina A&T State University（北卡罗来纳农工州立大学）；Tribhuvan University（特里布文大学）；University of Tennessee-Knoxville（田纳西大学诺克斯维尔分校）；University of South Dakota（南达科他大学）
Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation
School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子与信息工程学院）；School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications（南京邮电大学通信与信息工程学院）
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai（人工智能与未来网络研究院，北京师范大学，珠海）；School of Computer Science and Artificial Intelligence, Zhengzhou University（计算机科学与人工智能学院，郑州大学）；School of Software, Nanchang University（软件学院，南昌大学）
Two Stage Wireless Federated LoRA Fine-Tuning with Sparsified Orthogonal Updates
Department of Electrical and Computer Engineering, and the Institute of New Media and Communications, Seoul National University (SNU)（电气与计算机工程系，以及新媒体与通讯研究所，首尔国立大学）
