  In this study, we address the emerging field of Streaming Federated Learning
(SFL) and propose local cache update rules to manage dynamic data distributions
and limited cache capacity. Traditional federated learning relies on fixed data
sets, whereas in SFL, data is streamed, and its distribution changes over time,
leading to discrepancies between the local training dataset and long-term
distribution. To mitigate this problem, we propose three local cache update
rules - First-In-First-Out (FIFO), Static Ratio Selective Replacement (SRSR),
and Dynamic Ratio Selective Replacement (DRSR) - that update the local cache of
each client while considering the limited cache capacity. Furthermore, we
derive a convergence bound for our proposed SFL algorithm as a function of the
distribution discrepancy between the long-term data distribution and the
client's local training dataset. We then evaluate our proposed algorithm on two
datasets: a network traffic classification dataset and an image classification
dataset. Our experimental results demonstrate that our proposed local cache
update rules significantly reduce the distribution discrepancy and outperform
the baseline methods. Our study advances the field of SFL and provides
practical cache management solutions in federated learning.
