  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.
