Federated Learning: A signal processing perspective

By Tomer Gafni, Nir Shlezinger, Kobi Cohen, Yonina C. Eldar, H. Vincent Poor. 2021

169 citations recorded by the source corpus.

The dramatic success of deep learning is largely due to the availability of data. Data samples are often acquired on edge devices, such as smartphones, vehicles, and sensors, and in some cases cannot be shared due to privacy considerations. Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. Learning in a federated manner differs from conventional centralized machine learning and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications. Consequently, dedicated schemes derived from these areas are expected to play an important role in the success of federated learning and the transition of deep learning from the domain of centralized servers to mobile edge devices.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Robust Aggregation for Federated Learning (2019, background): “The federated learning algorithm, and in particular, the choice of aggregation, impacts the following three factors [34, 46, 56]: communication efficiency, privacy, and robustness.”
Model-Based Deep Learning (2020, background): “, through the framework of federated learning [111], [112]) and collaborative inference [113].”