  Ubiquitous systems with End-Edge-Cloud architecture are increasingly being
used in healthcare applications. Federated Learning (FL) is highly useful for
such applications, due to silo effect and privacy preserving. Existing FL
approaches generally do not account for disparities in the quality of local
data labels. However, the clients in ubiquitous systems tend to suffer from
label noise due to varying skill-levels, biases or malicious tampering of the
annotators. In this paper, we propose Federated Opportunistic Computing for
Ubiquitous Systems (FOCUS) to address this challenge. It maintains a small set
of benchmark samples on the FL server and quantifies the credibility of the
client local data without directly observing them by computing the mutual
cross-entropy between performance of the FL model on the local datasets and
that of the client local FL model on the benchmark dataset. Then, a credit
weighted orchestration is performed to adjust the weight assigned to clients in
the FL model based on their credibility values. FOCUS has been experimentally
evaluated on both synthetic data and real-world data. The results show that it
effectively identifies clients with noisy labels and reduces their impact on
the model performance, thereby significantly outperforming existing FL
approaches.
