{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T05:49:59Z","timestamp":1767678599771,"version":"3.48.0"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,19]]},"DOI":"10.1109\/aiccsa66935.2025.11315401","type":"proceedings-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:35:55Z","timestamp":1767638155000},"page":"1-10","source":"Crossref","is-referenced-by-count":0,"title":["RBFL: Securing Federated Learning against Data Poisoning Using a Reputation-based Approach"],"prefix":"10.1109","author":[{"given":"Abdul","family":"Rehman","sequence":"first","affiliation":[{"name":"Université du Québec á Chicoutimi,Quebec,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Issiaka Ischolla","family":"Mazu","sequence":"additional","affiliation":[{"name":"Université du Québec á Chicoutimi,Quebec,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fehmi","family":"Jaafar","sequence":"additional","affiliation":[{"name":"Université du Québec á Chicoutimi,Quebec,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Darine","family":"Ameyed","sequence":"additional","affiliation":[{"name":"École de Technologie Supérieure (ÉTS),Quebec,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamdi Ben","family":"Abdessalem","sequence":"additional","affiliation":[{"name":"Université du Québec á Chicoutimi,Quebec,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Advances and Open Problems in Federated Learning","author":"Kairouz","year":"2021","journal-title":"arXiv:1912.04977"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1108\/IJWIS-04-2022-0080"},{"article-title":"Federated Learning, ser. Synthesis Lectures on Artificial Intelligence and Machine Learning","volume-title":"Morgan & Claypool Publishers","author":"Yang","key":"ref3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-18523-6_12"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s43926-025-00108-6"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2024.3377931"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3605098.3635896"},{"article-title":"Federated learning based on defending against data poisoning attacks in iot","year":"2022","author":"Li","key":"ref8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2023.3296885"},{"article-title":"Gtg-shapley: Efficient and accurate participant contribution evaluation in federated learning","year":"2021","author":"Liu","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2940820"},{"article-title":"A reputation mechanism is all you need: Collaborative fairness and adversarial robustness in federated learning","year":"2020","author":"Xu","key":"ref12"},{"article-title":"Communication-efficient learning of deep networks from decentralized data","year":"2016","author":"McMahan","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CITSM.2016.7577578"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1515\/9781400881970-018"},{"key":"ref16","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proceedings of the 38th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research","volume":"139","author":"Li"},{"article-title":"On admm in heterogeneous federated learning: Personalization, robustness, and fairness","year":"2024","author":"Zhu","key":"ref17"},{"article-title":"Privacy and robustness in federated learning: Attacks and defenses","year":"2022","author":"Lyu","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-44-319037-7.00024-7"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v35i12.17263","article-title":"Towards understanding the influence of individual clients in federated learning","author":"Xue","year":"2021"},{"key":"ref21","doi-asserted-by":"crossref","DOI":"10.1109\/BigData47090.2019.9006179","article-title":"Measure contribution of participants in federated learning","author":"Wang","year":"2019"},{"key":"ref22","first-page":"2242","article-title":"Data shapley: Equitable valuation of data for machine learning","volume-title":"International Conference on Machine Learning","author":"Ghorbani"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3669828.3669837"},{"article-title":"What is your data worth? equitable valuation of data","volume-title":"by amirata ghorbani and james zou","author":"Ghorbani","key":"ref24"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342637"},{"first-page":"12020","article-title":"Focus: Dealing with label quality disparity in federated learning","author":"Chen","key":"ref26"},{"key":"ref27","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-63076-8_14","article-title":"Collaborative fairness in federated learning","author":"Lyu","year":"2020"},{"article-title":"Hierarchical federated learning incentivization for gas usage estimation","year":"2023","author":"Sun","key":"ref28"},{"first-page":"102020","article-title":"Collaborative machine learning with incentive-aware model rewards","author":"Sim","key":"ref29"},{"key":"ref30","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-63076-8_11","article-title":"A principled approach to data valuation for federated learning","author":"Wang","year":"2020"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.14778\/3659437.3659459"},{"key":"ref32","first-page":"10837","article-title":"Validation free and replication robust volume-based data valuation","volume":"34","author":"Xu","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref33","first-page":"16","article-title":"Gradient-driven rewards to guarantee fairness in collaborative machine learning","volume":"34","author":"Xu","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Towards fairness-aware federated learning","year":"2021","author":"Shi","key":"ref34"},{"first-page":"22024","article-title":"Towards fair, robust and efficient client contribution evaluation in federated learning","author":"Zhang","key":"ref35"},{"article-title":"Don\u2019t forget what i did?: Assessing client contributions in federated learning","year":"2024","author":"Ghosh","key":"ref36"},{"article-title":"Contribution evaluation in federated learning: Examining current approaches","year":"2023","author":"Siomos","key":"ref37"},{"key":"ref38","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v36i11.21505","article-title":"Contribution-aware federated learning for smart healthcare","author":"Liu","year":"2022"},{"article-title":"Communication-efficient learning of deep networks from decentralized data","year":"2016","author":"McMahan","key":"ref39"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3109\/14767050009053454"},{"key":"ref42","article-title":"Adult","author":"Becker","year":"1996","journal-title":"UCI Machine Learning Repository"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1162\/089976699300016304"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-29044-2"}],"event":{"name":"2025 IEEE\/ACS 22nd International Conference on Computer Systems and Applications (AICCSA)","start":{"date-parts":[[2025,10,19]]},"location":"Doha, Qatar","end":{"date-parts":[[2025,10,22]]}},"container-title":["2025 IEEE\/ACS 22nd International Conference on Computer Systems and Applications (AICCSA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11315137\/11315140\/11315401.pdf?arnumber=11315401","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T05:45:20Z","timestamp":1767678320000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11315401\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/aiccsa66935.2025.11315401","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}