Although federated learning has achieved many breakthroughs recently, the heterogeneous nature of the learning environment greatly limits its performance and hinders its real-world applications. The heterogeneous data, time-varying wireless conditions and computing-limited devices are three main challenges, which often result in an unstable training process and degraded accuracy. Herein, we propose strategies to address these challenges. Targeting the heterogeneous data distribution, we propose a novel adaptive mixing aggregation (AMA) scheme that mixes the model updates from previous rounds with current rounds to avoid large model shifts and thus, maintain training stability. We further propose a novel staleness-based weighting scheme for the asynchronous model updates caused by the dynamic wireless environment. Lastly, we propose a novel CPU-friendly computation-reduction scheme based on transfer learning by sharing the feature extractor (FES) and letting the computing-limited devices update only the classifier. The simulation results show that the proposed framework outperforms existing state-of-the-art solutions and increases the test accuracy, and training stability by up to 2.38%, 93.10% respectively. Additionally, the proposed framework can tolerate communication delay of up to 15 rounds under a moderate delay environment without significant accuracy degradation.

翻译：尽管联邦学习近期取得了诸多突破性进展，但学习环境的异构性严重制约了其性能表现，并阻碍了实际应用部署。异构数据分布、时变无线通信条件与计算能力受限的设备是三大核心挑战，常导致训练过程不稳定及精度下降。本文提出针对性解决方案：针对异构数据分布，提出新型自适应混合聚合（AMA）机制，通过混合前后轮次的模型更新来避免模型剧烈偏移，从而维持训练稳定性；针对动态无线环境引发的异步模型更新，提出基于陈旧度的加权方案；最后，基于迁移学习理念提出一种兼容CPU的计算量缩减方案，通过共享特征提取器（FES）使计算受限设备仅更新分类器。仿真结果表明，所提框架在测试精度与训练稳定性上较现有最优方案分别提升最高达2.38%与93.10%，且在中等延迟环境下可容忍最多15轮通信延迟而不显著降低精度。