OFL: Opportunistic Federated Learning for Resource-Heterogeneous and Privacy-Aware Devices

• URL: http://arxiv.org/abs/2503.15015v1
• Date: Wed, 19 Mar 2025 09:12:47 GMT
• Title: OFL: Opportunistic Federated Learning for Resource-Heterogeneous and Privacy-Aware Devices
• Authors: Yunlong Mao, Mingyang Niu, Ziqin Dang, Chengxi Li, Hanning Xia, Yuejuan Zhu, Haoyu Bian, Yuan Zhang, Jingyu Hua, Sheng Zhong,
• Abstract summary: Opportunistic Federated Learning (OFL) is a novel FL framework designed explicitly for resource-heterogenous and privacy-aware FL devices.<n>OFL provides strong security by introducing a differentially private and opportunistic model updating mechanism.<n>OFL achieves satisfying model performance and improves efficiency and security, outperforming existing solutions.
• Score: 8.037795580924163
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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