  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
