  Federated Learning (FL) enables model development by leveraging data
distributed across numerous edge devices without transferring local data to a
central server. However, existing FL methods still face challenges when dealing
with scarce and label-skewed data across devices, resulting in local model
overfitting and drift, consequently hindering the performance of the global
model. In response to these challenges, we propose a pioneering framework
called \textit{FLea}, incorporating the following key components: \textit{i)} A
global feature buffer that stores activation-target pairs shared from multiple
clients to support local training. This design mitigates local model drift
caused by the absence of certain classes; \textit{ii)} A feature augmentation
approach based on local and global activation mix-ups for local training. This
strategy enlarges the training samples, thereby reducing the risk of local
overfitting; \textit{iii)} An obfuscation method to minimize the correlation
between intermediate activations and the source data, enhancing the privacy of
shared features. To verify the superiority of \textit{FLea}, we conduct
extensive experiments using a wide range of data modalities, simulating
different levels of local data scarcity and label skew. The results demonstrate
that \textit{FLea} consistently outperforms state-of-the-art FL counterparts
(among 13 of the experimented 18 settings, the improvement is over $5\%$) while
concurrently mitigating the privacy vulnerabilities associated with shared
features. Code is available at https://github.com/XTxiatong/FLea.git
