Bias mitigationClient selection strategies have become a widely adopted approach in recent years within the studies on Federated Learning (FL). This strategy aims to handle the communication efficiency problem between the server and clients. However, due to certain differences in data distribution among...
Federated Learning (FL) is a decentralized machine learning (ML) approach that keeps data localized and often incorporates Differential Privacy (DP) to enhance privacy guarantees. Similar to previous work on DP in ML, we observed that differentially private federated learning (DPFL) introduces perform...
The training sample bias occurs when samples do not accurately reflect the context in which the learning method will be operated. In the medical domain, an expert to define a problem is used as a data labeling task. The method takes advantage of the “human in the loop” to ensure that ...
We study Sn and Qn by means of their influence functions, their bias curves (for implosion as well as explosion), and their finite-sample performance. Their behavior is also compared at non-Gaussian models, including the negative exponential model where Sn has a lower gross-error sensitivity ...
A bias introduced by the data governance is identified based on a core feature being affected by the data governance. In response to identifying a bias, an anti-bias procedure is applied on the machine learning model, whereby mitigating the bias introduced by the data governance. 展开 ...
Clock errors can be present on the satellite or receiver and can be caused by ephermis errors, receiver clock drift and bias, and measurement error. Ionospheric delay is a function of electron density along the signal’s propagation path. Tropospheric delay is a function of environmental condition...