{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T17:13:50Z","timestamp":1746551630013,"version":"3.32.0"},"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":[[2024,8]]},"abstract":"<jats:p>Federated Learning (FL) addresses the challenges posed by data silos, which arise from privacy, security regulations, and ownership concerns. Despite these barriers, FL enables these isolated data repositories to participate in collaborative learning without compromising privacy or security. Concurrently, the advancement of blockchain technology and decentralized applications (DApps) within Web 3.0 heralds a new era of transformative possibilities in web development. As such, incorporating FL into Web 3.0 paves the path for overcoming the limitations of data silos through collaborative learning. However, given the transaction speed constraints of core blockchains such as Ethereum (ETH) and the latency in smart contracts, employing one-shot FL, which minimizes client-server interactions in traditional FL to a single exchange, is considered more apt for Web 3.0 environments. This paper presents a practical one-shot FL system for Web 3.0, termed OFL-W3. OFL-W3 capitalizes on blockchain technology by utilizing smart contracts for managing transactions. Meanwhile, OFL-W3 utilizes the Inter-Planetary File System (IPFS) coupled with Flask communication, to facilitate backend server operations to use existing one-shot FL algorithms. With the integration of the incentive mechanism, OFL-W3 showcases an effective implementation of one-shot FL on Web 3.0, offering valuable insights and future directions for AI combined with Web 3.0 studies.<\/jats:p>","DOI":"10.14778\/3685800.3685900","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T17:25:21Z","timestamp":1731086721000},"page":"4461-4464","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["OFL-W3: A One-Shot Federated Learning System on Web 3.0"],"prefix":"10.14778","volume":"17","author":[{"given":"Linshan","family":"Jiang","sequence":"first","affiliation":[{"name":"National University of Singapore"}]},{"given":"Moming","family":"Duan","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"Bingsheng","family":"He","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"Yulin","family":"Sun","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Peishen","family":"Yan","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Yang","family":"Hua","sequence":"additional","affiliation":[{"name":"Queen's University Belfast"}]},{"given":"Tao","family":"Song","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Poster: A Reliable and Accountable Privacy-Preserving Federated Learning Framework Using the Blockchain. In CCS.","author":"Awan Sana","year":"2019","unstructured":"Sana Awan, Fengjun Li, Bo Luo, and Mei Liu. 2019. Poster: A Reliable and Accountable Privacy-Preserving Federated Learning Framework Using the Blockchain. In CCS."},{"key":"e_1_2_1_2_1","unstructured":"Juan Benet. 2014. IPFS - Content Addressed Versioned P2P File System. arXiv preprint arXiv:1407.3561."},{"volume-title":"Ethereum Gas Price Statistics","author":"Carl David","key":"e_1_2_1_3_1","unstructured":"David Carl and Christian Ewerhart. 2020. Ethereum Gas Price Statistics. University of Zurich, Department of Economics, Working Paper No. 373."},{"key":"e_1_2_1_4_1","volume-title":"Mustafa Safa Ozdayi, and Murat Kantarcioglu","author":"Desai Harsh Bimal","year":"2021","unstructured":"Harsh Bimal Desai, Mustafa Safa Ozdayi, and Murat Kantarcioglu. 2021. Blockfla: Accountable Federated Learning via Hybrid Blockchain Architecture. In CODASPY."},{"key":"e_1_2_1_5_1","unstructured":"Yiqun Diao Qinbin Li and Bingsheng He. 2023. Towards Addressing Label Skews in One-Shot Federated Learning. In ICLR."},{"key":"e_1_2_1_6_1","unstructured":"Neel Guha Ameet Talwalkar and Virginia Smith. 2019. One-Shot Federated Learning. arXiv preprint arXiv:1902.11175."},{"key":"e_1_2_1_7_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In AISTATS."},{"key":"e_1_2_1_8_1","volume-title":"Blockflow: An Accountable and Privacy-Preserving Solution for Federated Learning. arXiv preprint arXiv:2007.03856.","author":"Mugunthan Vaikkunth","year":"2020","unstructured":"Vaikkunth Mugunthan, Ravi Rahman, and Lalana Kagal. 2020. Blockflow: An Accountable and Privacy-Preserving Solution for Federated Learning. arXiv preprint arXiv:2007.03856."},{"key":"e_1_2_1_9_1","volume-title":"Ethereum: A Secure Decentralised Generalised Transaction Ledger. Ethereum Project Yellow Paper.","author":"Wood Gavin","year":"2014","unstructured":"Gavin Wood and Gavin Parity. 2014. Ethereum: A Secure Decentralised Generalised Transaction Ledger. Ethereum Project Yellow Paper."},{"key":"e_1_2_1_10_1","unstructured":"Mikhail Yurochkin Mayank Agarwal Soumya Ghosh Kristjan Greenewald Nghia Hoang and Yasaman Khazaeni. 2019. Bayesian Nonparametric Federated Learning of Neural Networks. In ICML."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3685800.3685900","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:27:28Z","timestamp":1735622848000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3685800.3685900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":10,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["10.14778\/3685800.3685900"],"URL":"https:\/\/doi.org\/10.14778\/3685800.3685900","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"2024-11-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}