Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges

There is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data to central ML processors. As a result, decentralized ML approaches that keep the data where it is generated are much more appealing. Due to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. In this article, we provide an accessible introduction to the general idea of federated learning, discuss several possible applications in 5G networks, and describe key technical challenges and open problems for future research on federated learning in the context of wireless communications.

[1]  Walid Saad,et al.  Federated Learning for Ultra-Reliable Low-Latency V2V Communications , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[2]  Tassilo Klein,et al.  Differentially Private Federated Learning: A Client Level Perspective , 2017, ArXiv.

[3]  Tianjian Chen,et al.  Federated Machine Learning: Concept and Applications , 2019 .

[4]  Sarvar Patel,et al.  Practical Secure Aggregation for Federated Learning on User-Held Data , 2016, ArXiv.

[5]  Yang Song,et al.  Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning , 2018, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[6]  Balasubramaniam Natarajan,et al.  A Multiband OFDMA Heterogeneous Network for Millimeter Wave 5G Wireless Applications , 2016, IEEE Access.

[7]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[8]  Hubert Eichner,et al.  Towards Federated Learning at Scale: System Design , 2019, SysML.

[9]  Osvaldo Simeone,et al.  A Very Brief Introduction to Machine Learning With Applications to Communication Systems , 2018, IEEE Transactions on Cognitive Communications and Networking.

[10]  Úlfar Erlingsson,et al.  The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks , 2018, USENIX Security Symposium.

[11]  Vitaly Shmatikov,et al.  Exploiting Unintended Feature Leakage in Collaborative Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).

[12]  Kin K. Leung,et al.  Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.

[13]  Prateek Mittal,et al.  Analyzing Federated Learning through an Adversarial Lens , 2018, ICML.

[14]  Ridha Soua,et al.  Improved operator experience through Experiential Networked Intelligence , 2017 .