Amplified contribution analysis for federated learning

The problem of establishing the client’s marginal contribution is essential to any decentralised machine-learning process that relies on the participation of remote agents. The ability to detect harmful participants on an ongoing basis can constitute a significant challenge as one can obtain only a very limited amount of information from the external environment in order not to break the privacy assumption that underlies the federated learning paradigm. In this work, we present an Amplified Contribution Function - a set of aggregation operations performed on gradients received by the central orchestrator that allows to non-intrusively investigate the risk of accepting a certain set of gradients dispatched from a remote agent. Our proposed method is distinguished by a high degree of interpretability and interoperability as it supports the gross majority of the currently available federated techniques and algorithms. It is also characterised by a space and time complexity similar to that of the leave-one-out method - a common baseline for all deletion and sensitivity analytics tools.