Distributed Machine Learning and Wireless Networks: A Closer Union | AI/ML IN 5G CHALLENGE
Due to major communication, privacy, and scalability challenges stemming from the emergence of large-scale Internet of Things services, machine learning is witnessing a major departure from traditional centralized cloud architectures toward a distributed machine learning (ML) paradigm where data is dispersed and processed across multiple edge devices. A prime example of this emerging distributed ML paradigm is Google’s renowned federated learning framework. Such distributed ML paradigms provide two main opportunities for wireless and 5G research. On the one hand, inspired by these developments, one can design a plethora of wireless-oriented, AI-native distributed ML frameworks to enable the self-optimization and self-management of wireless systems. On the other hand, emerging 5G/6G applications such as connected autonomy will be powered by distributed ML algorithms and, thus, distributed ML constitutes an important and unique use case that must be supported by current and future wireless systems.
In this talk, we provide an overview of these two use cases, and we shed light on how distributed ML can be a key enabler for the design, deployment, and management of future wireless systems and their associated technologies and applications.
Speaker:
Walid Saad, Professor at the Department of Electrical and Computer Engineering, Virginia Tech
Shownotes:
00:00 Intro
1:57 opening presentation
6:43 Deep reinforcement learning for reliable communications
22:37 Generative Adversarial Networks (BGANS)
30:20 Multi-agent reinforcement learning with Meta-learning
42:37 Q&A
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