{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T16:27:01Z","timestamp":1759940821773,"version":"3.44.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Federated learning (FL) is a general distributed machine learning paradigm that provides solutions for tasks where data cannot be shared directly. Due to the difficulties in communication management and heterogeneity of distributed data and devices, initiating and using an FL algorithm for real-world cross-device scenarios requires significant repetitive effort but may not be transferable to similar projects. To reduce the effort required for developing and deploying FL algorithms, we present FS-Real, an open-source FL platform designed to address the need of a general and efficient infrastructure for real-world cross-device FL. In this paper, we introduce the key components of FS-Real and demonstrate that FS-Real has the following capabilities: 1) reducing the programming burden of FL algorithm development with plug-and-play and adaptable runtimes on Android and other Internet of Things (IoT) devices; 2) handling a large number of heterogeneous devices efficiently and robustly with our communication management components; 3) supporting a wide range of advanced FL algorithms with flexible configuration and extension; 4) alleviating the costs and efforts for deployment, evaluation, simulation, and performance optimization of FL algorithms with automatized tool kits.<\/jats:p>","DOI":"10.14778\/3611540.3611617","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:32:37Z","timestamp":1694777557000},"page":"4046-4049","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["FS-Real: A Real-World Cross-Device Federated Learning Platform"],"prefix":"10.14778","volume":"16","author":[{"given":"Dawei","family":"Gao","sequence":"first","affiliation":[{"name":"Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daoyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zitao","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuexiang","family":"Xie","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuchen","family":"Pan","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaliang","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bolin","family":"Ding","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingren","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"AISTATS'17","volume":"54","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, et al. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In AISTATS'17, Vol. 54. 1273--1282."},{"key":"e_1_2_1_2_1","unstructured":"Daoyuan Chen Dawei Gao Weirui Kuang et al. 2022. pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning. In NeurIPS'22 Datasets and Benchmarks Track."},{"key":"e_1_2_1_3_1","unstructured":"Daoyuan Chen Dawei Gao Yuexiang Xie et al. 2023. FS-Real: Towards Real-World Cross-Device Federated Learning. arXiv preprint:2303.13363 (2023)."},{"key":"e_1_2_1_4_1","volume-title":"Sanjay Sri Vallabh Singapuram, et al","author":"Lai Fan","year":"2022","unstructured":"Fan Lai, Yinwei Dai, Sanjay Sri Vallabh Singapuram, et al. 2022. FedScale: Bench-marking Model and System Performance of Federated Learning at Scale. In ICML'22, Vol. 162. 11814--11827."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"volume-title":"OSDI'22","year":"2022","key":"e_1_2_1_7_1","unstructured":"Lv, Chengfei, et al. 2022. Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning. In OSDI'22. 249--265."},{"volume-title":"AISTATS'22","year":"2022","key":"e_1_2_1_8_1","unstructured":"Nguyen, John, et al. 2022. Federated learning with buffered asynchronous aggregation. In AISTATS'22. 3581--3607."},{"key":"e_1_2_1_9_1","volume-title":"FedBABU: Toward Enhanced Representation for Federated Image Classification. In ICLR'22","author":"Oh Jaehoon","year":"2022","unstructured":"Jaehoon Oh, SangMook Kim, and Se-Young Yun. 2022. FedBABU: Toward Enhanced Representation for Federated Image Classification. In ICLR'22."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3579075.3579081"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3611540.3611617","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:34:44Z","timestamp":1757543684000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3611540.3611617"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":10,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.14778\/3611540.3611617"],"URL":"https:\/\/doi.org\/10.14778\/3611540.3611617","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"2023-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}