홈 › Papers

A Benchmark for Federated Hetero-Task Learning

2022-06-07 · Liuyi Yao, Dawei Gao, Zhen Wang, Yuexiang Xie, Weirui Kuang, Daoyuan Chen, Haohui Wang, Chenhe Dong, Bolin Ding, Yaliang Li

To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks. We also present B-FHTL, a federated hetero-task learning benchmark consisting of simulation dataset, FL protocols and a unified evaluation mechanism. B-FHTL dataset contains three well-designed federated learning tasks with increasing heterogeneity. Each task simulates the clients with different non-IID data and learning tasks. To ensure fair comparison among different FL algorithms, B-FHTL builds in a full suite of FL protocols by providing high-level APIs to avoid privacy leakage, and presets most common evaluation metrics spanning across different learning tasks, such as regression, classification, text generation and etc. Furthermore, we compare the FL algorithms in fields of federated multi-task learning, federated personalization and federated meta learning within B-FHTL, and highlight the influence of heterogeneity and difficulties of federated hetero-task learning. Our benchmark, including the federated dataset, protocols, the evaluation mechanism and the preliminary experiment, is open-sourced at https://github.com/alibaba/FederatedScope/tree/master/benchmark/B-FHTL

📄 PDF Abstract BibTeX arXiv:2206.03436

Code (1)

alibaba/federatedscope 공식 구현 pytorch

Tasks

Federated Learning Meta-Learning Multi-Task Learning Text Generation

Benchmarking Algorithms for Federated Domain Generalization

2023-07-11 · Ruqi Bai, Saurabh Bagchi, David I. Inouye

While prior domain generalization (DG) benchmarks consider train-test dataset heterogeneity, we evaluate Federated DG which introduces federated learning (FL) specific challenges. Additionally, we explore domain-based he…

Benchmarking Diversity Domain Generalization Federated Learning

Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction

2025-01-21 · Jia-Hao Syu, Jerry Chun-Wei Lin

In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm with heterogeneous transfers. We design den…

Federated Learning Prediction Time Series Time Series Prediction+1

Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation

2024-12-11 · SeungBum Ha, Taehwan Lee, Jiyoun Lim, Sung Whan Yoon

Federated learning (FL) has recently garnered attention as a data-decentralized training framework that enables the learning of deep models from locally distributed samples while keeping data privacy. Built upon the fram…

Benchmarking Federated Learning Graph Generation Panoptic Scene Graph Generation+1

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning

2022-06-18 · Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao 외

Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been purported to improve metrics such as accuracy, fairness, and robu…

Benchmarking Fairness Federated Learning Meta-Learning+3

Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning

2022-10-14 · John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi 외

An oft-cited challenge of federated learning is the presence of heterogeneity. \emph{Data heterogeneity} refers to the fact that data from different clients may follow very different distributions. \emph{System heterogen…

Federated Learning