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Overview: The book reports state of the art results in Software Engineering Research, Management & Applications in both printed and electronic form. SCI (Studies in Computation Intelligence) has grown into the most comprehensive computational intelligence research forum available in the world. Federated Leaning (FL), as a distributed Machine Learning paradigm, enables the collaboration of a group of agents to collaboratively conduct a learning model training, while preserving the data privacy. Federated Learning is viable distributed Machine Learning framework that can be applied to different smart-world systems, supported by the advancement of rapidly growing networking and computing techniques. Federated Learning (FL) has made possible the collaborative training of Machine Learning models between aggregation server and clients without sharing their private data. With the massive volume of heterogeneous data from various clients, the server faces challenges such as data unbalance, data corruption, and/or data irrelevancy. As a result, the FL setting is exposed to numerous security risks that lead to performance deterioration of learning effectiveness. To tackle the issue, in this paper we propose the Heterogeneity Index Based Clustering (HIC) approach, which enables the dynamic categorization of clients into clusters. Particularly, the model weights are dynamically clustered based on their heterogeneity level using an affinity propagation method. The HIC approach uses a simple, but effective way of scaling data heterogeneity and dynamic clustering to create a resilient learning system against backdoor attacks that outperforms the existing works on FL robustness.
Genre: Non-Fiction > Tech & Devices
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