Abstract
Machine learning over distributed data collected by many clients has important applications in use cases where data privacy is a key concern or central data storage is not an option. Federated learning has introduced solutions for these scenarios, unlike the client-server approach, where all the training data is centralized in the server side, the clients, in a federated learning approach, perform machine learning updates locally over their data and the central server merely aggregates the resulting models without accessing the client’s local data. Article reviews the characteristics and learning objectives of the federated learning setting and gives an overview of the base algorithms that have been developed for FL, compare between FEDAVG and Consensus algorithm and shows the drawbacks and advantages of Consensus algorithm.
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Ali, A. (2023). Federated Learning Strategies Over Wireless Channels. In: Koucheryavy, Y., Aziz, A. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN 2022. Lecture Notes in Computer Science, vol 13772. Springer, Cham. https://doi.org/10.1007/978-3-031-30258-9_47
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DOI: https://doi.org/10.1007/978-3-031-30258-9_47
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