Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2503.15015 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913744850780160 |
|---|---|
| author | Mao, Yunlong Niu, Mingyang Dang, Ziqin Li, Chengxi Xia, Hanning Zhu, Yuejuan Bian, Haoyu Zhang, Yuan Hua, Jingyu Zhong, Sheng |
| author_facet | Mao, Yunlong Niu, Mingyang Dang, Ziqin Li, Chengxi Xia, Hanning Zhu, Yuejuan Bian, Haoyu Zhang, Yuan Hua, Jingyu Zhong, Sheng |
| contents | Efficient and secure federated learning (FL) is a critical challenge for resource-limited devices, especially mobile devices. Existing secure FL solutions commonly incur significant overhead, leading to a contradiction between efficiency and security. As a result, these two concerns are typically addressed separately. This paper proposes Opportunistic Federated Learning (OFL), a novel FL framework designed explicitly for resource-heterogenous and privacy-aware FL devices, solving efficiency and security problems jointly. OFL optimizes resource utilization and adaptability across diverse devices by adopting a novel hierarchical and asynchronous aggregation strategy. OFL provides strong security by introducing a differentially private and opportunistic model updating mechanism for intra-cluster model aggregation and an advanced threshold homomorphic encryption scheme for inter-cluster aggregation. Moreover, OFL secures global model aggregation by implementing poisoning attack detection using frequency analysis while keeping models encrypted. We have implemented OFL in a real-world testbed and evaluated OFL comprehensively. The evaluation results demonstrate that OFL achieves satisfying model performance and improves efficiency and security, outperforming existing solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OFL: Opportunistic Federated Learning for Resource-Heterogeneous and Privacy-Aware Devices Mao, Yunlong Niu, Mingyang Dang, Ziqin Li, Chengxi Xia, Hanning Zhu, Yuejuan Bian, Haoyu Zhang, Yuan Hua, Jingyu Zhong, Sheng Cryptography and Security Efficient and secure federated learning (FL) is a critical challenge for resource-limited devices, especially mobile devices. Existing secure FL solutions commonly incur significant overhead, leading to a contradiction between efficiency and security. As a result, these two concerns are typically addressed separately. This paper proposes Opportunistic Federated Learning (OFL), a novel FL framework designed explicitly for resource-heterogenous and privacy-aware FL devices, solving efficiency and security problems jointly. OFL optimizes resource utilization and adaptability across diverse devices by adopting a novel hierarchical and asynchronous aggregation strategy. OFL provides strong security by introducing a differentially private and opportunistic model updating mechanism for intra-cluster model aggregation and an advanced threshold homomorphic encryption scheme for inter-cluster aggregation. Moreover, OFL secures global model aggregation by implementing poisoning attack detection using frequency analysis while keeping models encrypted. We have implemented OFL in a real-world testbed and evaluated OFL comprehensively. The evaluation results demonstrate that OFL achieves satisfying model performance and improves efficiency and security, outperforming existing solutions. |
| title | OFL: Opportunistic Federated Learning for Resource-Heterogeneous and Privacy-Aware Devices |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2503.15015 |