Online-Score-Aided Federated Learning: Taming the Resource Constraints in Wireless Networks

While federated learning (FL) is a widely popular distributed machine learning (ML) strategy that protects data privacy, time-varying wireless network parameters and heterogeneous configurations of the wireless devices pose significant challenges. Although the limited radio and computational resources of the network and the clients, respectively, are widely acknowledged, two critical yet often ignored aspects are (a) wireless devices can only dedicate a small chunk of their limited storage for the FL task and (b) new training samples may arrive in an online manner in many practical wireless applications. Therefore, we propose a new FL algorithm called online-score-aided federated learning (OSAFL), specifically designed to learn tasks relevant to wireless applications under these practical considerations. Since clients' local training steps differ under resource constraints, which may lead to client drift under statistically heterogeneous data distributions, we leverage normalized gradient similarities and exploit weighting clients' updates based on optimized scores that facilitate the convergence rate of the proposed OSAFL algorithm without incurring any communication overheads to the clients or requiring any statistical data information from them. Our extensive simulation results on two different datasets with four popular ML models validate the effectiveness of OSAFL compared to five modified state-of-the-art FL baselines.

@article{pervej2025_2408.05886,
  title={ Online-Score-Aided Federated Learning: Taming the Resource Constraints in Wireless Networks },
  author={ Md-Ferdous Pervej and Minseok Choi and Andreas F. Molisch },
  journal={arXiv preprint arXiv:2408.05886},
  year={ 2025 }
}