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Internet Technol."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>Foundation models (FMs) have achieved state-of-the-art performance across various domains, benefiting from their vast number of parameters and the extensive amount of publicly available training data. However, real-world deployments reveal challenges such as system heterogeneity, where not all devices can handle the complexity of FMs, and emerging privacy concerns that limit the availability of public data. To address these challenges, we propose HeLoRA, a novel approach combining low-rank adaptation (LoRA) with federated learning to enable heterogeneous federated fine-tuning. HeLoRA allows clients to fine-tune models with different complexities by adjusting the rank values of LoRA matrices, tailoring the process to each device\u2019s capabilities. To tackle the challenge of aggregating models with different structures, HeLoRA introduces two variants, i.e., HeLoRA-Pad and HeLoRA-KD. HeLoRA-Pad employs context-based padding to standardize the LoRA matrices, aligning them with the global model through a rank-based adaptive aggregation strategy. In contrast, HeLoRA-KD leverages the idea of deep mutual learning for aggregation, allowing heterogeneous models to retain their original structures. Extensive experiments with various datasets and ablation studies demonstrate that HeLoRA outperforms existing baselines, promising to enhance the practical deployment of FMs in diverse real-world environments.<\/jats:p>","DOI":"10.1145\/3723877","type":"journal-article","created":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T11:24:26Z","timestamp":1742037866000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["HeLoRA: LoRA-heterogeneous Federated Fine-tuning for Foundation Models"],"prefix":"10.1145","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0790-6035","authenticated-orcid":false,"given":"Boyu","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5945-9551","authenticated-orcid":false,"given":"Xiang","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Helsinki, Helsinki, Finland and Department of Agricultural Sciences, University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4220-3650","authenticated-orcid":false,"given":"Sasu","family":"Tarkoma","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Helsinki, Helsinki, Finland and Center for Ubiquitous Computing, University of Oulu, Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6026-1083","authenticated-orcid":false,"given":"Pan","family":"Hui","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology Guangzhou Information Hub, Guangzhou, China and Department of Computer Science, University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"29677","volume-title":"Advances in Neural Information Processing Systems","author":"Alam Samiul","year":"2022","unstructured":"Samiul Alam, Luyang Liu, Ming Yan, and Mi Zhang. 2022. 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