Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

By Jihong Park, Sumudu Samarakoon, Anis Elgabli, Joongheon Kim, Medhi Bennis, Seong-Lyun Kim, Merouane Debbah. 2020

185 citations recorded by the source corpus.

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under the time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles and, thereby, present communication-efficient and distributed learning frameworks with selected use cases.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning (2020, methodology): “INTRODUCTION Wireless connectivity has a great potential to scale up federated learning (FL) [1]–[3] by cutting the wires between workers and their parameter server (PS) [4]–[13].”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “update can be masked by using encryption or differential privacy techniques [13].”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, background): “The works in [9], [10], [11] discussed the key concepts of FL and its protocol, architectures, and applications in wireless networks.”
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges (2021, methodology): “In the context of distributed learning, the objective of 6G design is no longer rate maximization but to accelerate the training of ML models using distributed data [18]–[23].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “It can be typically applied to the medical diagnosis and millimeter wave channel prediction [37].”
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM (2020, background): “These applications commonly require extremely low latency and high reliability while accurately reacting to local environmental dynamics (Park et al., 2020b).”