  Federated learning (FL) is a promising approach for learning a model from
data distributed on massive clients without exposing data privacy. It works
effectively in the ideal federation where clients share homogeneous data
distribution and learning behavior. However, FL may fail to function
appropriately when the federation is not ideal, amid an unhealthy state called
Negative Federated Learning (NFL), in which most clients gain no benefit from
participating in FL. Many studies have tried to address NFL. However, their
solutions either (1) predetermine to prevent NFL in the entire learning
life-cycle or (2) tackle NFL in the aftermath of numerous learning rounds.
Thus, they either (1) indiscriminately incur extra costs even if FL can perform
well without such costs or (2) waste numerous learning rounds. Additionally,
none of the previous work takes into account the clients who may be
unwilling/unable to follow the proposed NFL solutions when using those
solutions to upgrade an FL system in use. This paper introduces FL-GUARD, a
holistic framework that can be employed on any FL system for tackling NFL in a
run-time paradigm. That is, to dynamically detect NFL at the early stage (tens
of rounds) of learning and then to activate recovery measures when necessary.
Specifically, we devise a cost-effective NFL detection mechanism, which relies
on an estimation of performance gain on clients. Only when NFL is detected, we
activate the NFL recovery process, in which each client learns in parallel an
adapted model when training the global model. Extensive experiment results
confirm the effectiveness of FL-GUARD in detecting NFL and recovering from NFL
to a healthy learning state. We also show that FL-GUARD is compatible with
previous NFL solutions and robust against clients unwilling/unable to take any
recovery measures.
