Federated Learning (FL) is a distributed machine learning (ML) paradigm,
aiming to train a global model by exploiting the decentralized data across
millions of edge devices. Compared with centralized learning, FL preserves the
clients' privacy by refraining from explicitly downloading their data. However,
given the geo-distributed edge devices (e.g., mobile, car, train, or subway)
with highly dynamic networks in the wild, aggregating all the model updates
from those participating devices will result in inevitable long-tail delays in
FL. This will significantly degrade the efficiency of the training process. To
resolve the high system heterogeneity in time-sensitive FL scenarios, we
propose a novel FL framework, DynamicFL, by considering the communication
dynamics and data quality across massive edge devices with a specially designed
client manipulation strategy. \ours actively selects clients for model updating
based on the network prediction from its dynamic network conditions and the
quality of its training data. Additionally, our long-term greedy strategy in
client selection tackles the problem of system performance degradation caused
by short-term scheduling in a dynamic network. Lastly, to balance the trade-off
between client performance evaluation and client manipulation granularity, we
dynamically adjust the length of the observation window in the training process
to optimize the long-term system efficiency. Compared with the state-of-the-art
client selection scheme in FL, \ours can achieve a better model accuracy while
consuming only 18.9\% -- 84.0\% of the wall-clock time. Our component-wise and
sensitivity studies further demonstrate the robustness of \ours under various
real-life scenarios.

Federated Learning aims to train a global model by utilizing decentralized data, but the highly dynamic networks of edge devices can cause delays and degrade the efficiency of the training process. To address this, DynamicFL is proposed as a novel framework that considers communication dynamics, data quality, and client selection strategies to improve system performance and achieve better model accuracy.