The rise of mobile devices with abundant sensory data and local computing capabilities has driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) emerges to train specific deep models for each mobile device to address data heterogeneity and varying performance preferences. However, mobile training times vary significantly, resulting in either delay (when waiting for slower devices for aggregation) or accuracy decline (when aggregation proceeds without waiting). In response, we propose a shift towards asynchronous PFL, where the server aggregates updates as soon as they are available. Nevertheless, existing asynchronous protocols are unfit for PFL because they are devised for federated training of a single global model. They suffer from slow convergence and decreased accuracy when confronted with severe data heterogeneity prevalent in PFL. Furthermore, they often exclude slower devices for staleness control, which notably compromises accuracy when these devices possess critical personalized data. Therefore, we propose EchoPFL, a coordination mechanism for asynchronous PFL. Central to EchoPFL is to include updates from all mobile devices regardless of their latency. To cope with the inevitable staleness from slow devices, EchoPFL revisits model broadcasting. It intelligently converts the unscalable broadcast to on-demand broadcast, leveraging the asymmetrical bandwidth in wireless networks and the dynamic clustering-based PFL. Experiments show that compared to status quo approaches, EchoPFL achieves a reduction of up to 88.2% in convergence time, an improvement of up to 46% in accuracy, and a decrease of 37% in communication costs

翻译：随着移动设备在感知数据和本地计算能力上的显著提升，联邦学习（FL）在移动设备上的应用已形成趋势。个性化联邦学习（PFL）应运而生，旨在为每台设备训练特定深度模型，以应对数据异构性和差异化性能偏好。然而，移动设备的训练时间差异显著，导致要么因等待慢速设备聚合而产生延迟，要么在不等待的情况下聚合导致精度下降。为此，我们提出转向异步PFL：服务器在收到更新后立即聚合。然而，现有异步协议并不适用于PFL，因为它们专为单一全局模型的联邦训练设计。面对PFL中普遍存在的严重数据异构性，这些协议收敛缓慢且精度下降。此外，它们常通过遗弃慢速设备控制陈旧性，但当这些设备持有关键个性化数据时，此举显著损害精度。因此，我们提出EchoPFL——一种异步PFL的协调机制。其核心在于包含所有移动设备的更新，无论其延迟如何。为应对慢速设备不可避免的陈旧性，EchoPFL重新审视模型广播机制：利用无线网络中的非对称带宽和基于动态聚类的PFL，智能地将不可扩展的广播转化为按需广播。实验表明，与现有方法相比，EchoPFL将收敛时间降低多达88.2%，精度提升高达46%，且通信成本减少37%。