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| Title | Federated Approach to Online Pairwise Comparison Ranking Problem |
| Creator | 蔡炎龍 Tsai, Yen-Lung;Lin, Tse-Yu |
| Contributor | 應數系 |
| Key Words | federated learning; pairwise comparison; HodgeRank |
| Date | 2024-01 |
| Date Issued | 12-Mar-2025 11:17:17 (UTC+8) |
| Summary | In this work, we propose a federated learning (FL) framework for the online HodgeRank problem to obtain a global ranking based on the pairwise comparison data provided by users while respecting users’ data privacy. HodgeRank is a statistical ranking method that views pairwise comparison data as an edge-weighted directed graph, and, vertex weight function can be viewed as a ranking that recovers edge weight using difference. A critical assumption of the HodgeRank is the connectivity of the comparison graph. This assumption relies on the contribution of users’ data to form a weakly connected directed graph between items to be ranked. In this paper, we aim to find a framework to compute HodgeRank with the minimum usage of users’ data. |
| Relation | 2024 IEEE International Conference on Consumer Electronics (ICCE), IEEE |
| Type | conference |
| DOI | https://doi.org/10.1109/ICCE59016.2024.10444309 |
| dc.contributor | 應數系 | |
| dc.creator (作者) | 蔡炎龍 | |
| dc.creator (作者) | Tsai, Yen-Lung;Lin, Tse-Yu | |
| dc.date (日期) | 2024-01 | |
| dc.date.accessioned | 12-Mar-2025 11:17:17 (UTC+8) | - |
| dc.date.available | 12-Mar-2025 11:17:17 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-Mar-2025 11:17:17 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/156217 | - |
| dc.description.abstract (摘要) | In this work, we propose a federated learning (FL) framework for the online HodgeRank problem to obtain a global ranking based on the pairwise comparison data provided by users while respecting users’ data privacy. HodgeRank is a statistical ranking method that views pairwise comparison data as an edge-weighted directed graph, and, vertex weight function can be viewed as a ranking that recovers edge weight using difference. A critical assumption of the HodgeRank is the connectivity of the comparison graph. This assumption relies on the contribution of users’ data to form a weakly connected directed graph between items to be ranked. In this paper, we aim to find a framework to compute HodgeRank with the minimum usage of users’ data. | |
| dc.format.extent | 111 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 2024 IEEE International Conference on Consumer Electronics (ICCE), IEEE | |
| dc.subject (關鍵詞) | federated learning; pairwise comparison; HodgeRank | |
| dc.title (題名) | Federated Approach to Online Pairwise Comparison Ranking Problem | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1109/ICCE59016.2024.10444309 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/ICCE59016.2024.10444309 |