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Within the TAI spectrum, federated learning (FL) emerges as a promising solution to safeguard personal information in distributed settings across a multitude of practical contexts. However, the realm of FL is not without its challenges. Especially worrisome are adversarial attacks targeting its algorithmic robustness and systemic confidentiality. Moreover, the presence of biases and opacity in prediction outcomes further complicates FL\u2019s broader adoption. Consequently, there is a growing expectation for FL to instill trust. To address this, we chart out a comprehensive road-map for\n            <jats:italic>Trustworthy Federated Learning (TFL)<\/jats:italic>\n            and provide an overview of existing efforts across four pivotal dimensions:\n            <jats:italic>Privacy and Security<\/jats:italic>\n            ,\n            <jats:italic>Robustness<\/jats:italic>\n            ,\n            <jats:italic>Fairness<\/jats:italic>\n            , and\n            <jats:italic>Explainability<\/jats:italic>\n            . For each dimension, we identify potential pitfalls that might undermine TFL and present a curated selection of defensive strategies, enriched by a discourse on technical solutions tailored for TFL. Furthermore, we present potential challenges and future directions to be explored for in-depth TFL research with broader impacts.\n          <\/jats:p>","DOI":"10.1145\/3678181","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T15:43:59Z","timestamp":1721749439000},"page":"1-47","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":57,"title":["A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4185-8663","authenticated-orcid":false,"given":"Yifei","family":"Zhang","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4517-5379","authenticated-orcid":false,"given":"Dun","family":"Zeng","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China and Peng Cheng Lab, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9705-7913","authenticated-orcid":false,"given":"Jinglong","family":"Luo","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, China and Peng Cheng Lab, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7195-2549","authenticated-orcid":false,"given":"Xinyu","family":"Fu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2685-8148","authenticated-orcid":false,"given":"Guanzhong","family":"Chen","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5550-6461","authenticated-orcid":false,"given":"Zenglin","family":"Xu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, China and Peng Cheng Lab, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8106-6447","authenticated-orcid":false,"given":"Irwin","family":"King","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR"}]}],"member":"320","published-online":{"date-parts":[[2024,10,19]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_1_3_2","unstructured":"Guillaume Alain Alex Lamb Chinnadhurai Sankar Aaron C. 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