TY - JOUR
T1 - Federated Learning
T2 - A signal processing perspective
AU - Gafni, Tomer
AU - Shlezinger, Nir
AU - Cohen, Kobi
AU - Eldar, Yonina C.
AU - Poor, H. Vincent
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - 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.
AB - 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.
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U2 - 10.1109/MSP.2021.3125282
DO - 10.1109/MSP.2021.3125282
M3 - Article
AN - SCOPUS:85130148022
SN - 1053-5888
VL - 39
SP - 14
EP - 41
JO - IEEE Audio and Electroacoustics Newsletter
JF - IEEE Audio and Electroacoustics Newsletter
IS - 3
ER -