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.
View on arXiv@article{mao2025_2503.15015,
title={ OFL: Opportunistic Federated Learning for Resource-Heterogeneous and Privacy-Aware Devices },
author={ Yunlong Mao and Mingyang Niu and Ziqin Dang and Chengxi Li and Hanning Xia and Yuejuan Zhu and Haoyu Bian and Yuan Zhang and Jingyu Hua and Sheng Zhong },
journal={arXiv preprint arXiv:2503.15015},
year={ 2025 }
}