  Federated Edge Learning (FEL) has emerged as a promising approach for
enabling edge devices to collaboratively train machine learning models while
preserving data privacy. Despite its advantages, practical FEL deployment faces
significant challenges related to device constraints and device-server
interactions, necessitating heterogeneous, user-adaptive model training with
limited and uncertain communication. In this paper, we introduce FedCache 2.0,
a novel personalized FEL architecture that simultaneously addresses these
challenges. FedCache 2.0 incorporates the benefits of both dataset distillation
and knowledge cache-driven federated learning by storing and organizing
distilled data as knowledge in the server-side knowledge cache. Moreover, a
device-centric cache sampling strategy is introduced to tailor transferred
knowledge for individual devices within controlled communication bandwidth.
Extensive experiments on five datasets covering image recognition, audio
understanding, and mobile sensor data mining tasks demonstrate that (1)
FedCache 2.0 significantly outperforms state-of-the-art methods regardless of
model structures, data distributions, and modalities. (2) FedCache 2.0 can
train splendid personalized on-device models with at least $\times$28.6
improvement in communication efficiency.
