  Federated learning has attracted attention in recent years for
collaboratively training data on distributed devices with privacy-preservation.
The limited network capacity of mobile and IoT devices has been seen as one of
the major challenges for cross-device federated learning. Recent solutions have
been focusing on threshold-based client selection schemes to guarantee the
communication efficiency. However, we find this approach can cause biased
client selection and results in deteriorated performance. Moreover, we find
that the challenge of network limit may be overstated in some cases and the
packet loss is not always harmful. In this paper, we explore the loss tolerant
federated learning (LT-FL) in terms of aggregation, fairness, and
personalization. We use ThrowRightAway (TRA) to accelerate the data uploading
for low-bandwidth-devices by intentionally ignoring some packet losses. The
results suggest that, with proper integration, TRA and other algorithms can
together guarantee the personalization and fairness performance in the face of
packet loss below a certain fraction (10%-30%).
