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

Niknam, Solmaz; Dhillon, Harpreet S.; Reed, Jeffrey H.

doi:10.1109/mcom.001.1900461

Cited by 482 publications

(203 citation statements)

References 4 publications

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“…Note that due to closed-loop exchanges (locally trained model update followed by a globally aggregated model update triggering the next iteration of local training), the delay in completing GFL training may sometimes be around a few minutes (10 or more). As recorded recently for the Google keyboard programme [11]. This is not permissible in UAVs applications considered in this work.…”

Section: Federated Learning With Blockchainmentioning

confidence: 99%

Federated learning meets blockchain at 6G edge

Pokhrel

2020

Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond

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We consider a new blockchain empowered federated learning approach which uses wireless mobile miners at drones in the future sixth generation (6G) networks for a disaster response system. Our focus is on the blockchain latency, and energy consumption in the proposed architecture of the network of drones. Maintaining low delay in wireless communication between the drones is required to minimize blockchain forking events while performing blockchain operations. Therefore, we quantify the probability of occurrence of forking events to analyze the uncertainty of the system towards the additional energy wastage. The forked block (due to channel impairments or mobility) incurs re-computation energy. We develop pragmatic analyses of the expected energy consumption by considering the parameters like the number of miners as well as the power consumed during computing, block transfer and 6G channel dynamics for the system. CCS CONCEPTS • Networks → Network performance analysis; • Computing methodologies → Distributed computing methodologies.

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Section: Federated Learning With Blockchainmentioning

confidence: 99%

Federated learning meets blockchain at 6G edge

Pokhrel

2020

Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond

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“…In [24], [37], [39], [41], [87]- [90], it was discussed that FL is a promising technique for future intelligent networks due to its superior performance features and added benefits. Edge caching solutions which are based on FL algorithms can guarantee smart models, reduced content delivery latency, improved content acquisition reliability, and improved energy efficiency, all while ensuring preservation of personal data privacy and security.…”

Section: F Edge Cachingmentioning

confidence: 99%

“…However, traditional DL schemes are cloudcentric and they require a stream of raw training data to be sent and processed in a centralized server [39], [74]. The process of sending streams of raw training data to a centralized server can result in several challenges, including slow response to real-time events in latency sensitive applications, excessive network communication resource consumption, increased network traffic, high energy consumption, and reduced privacy of training data [39], [43], [79], [87], [124], [137], [138]. Therefore, traditional DL frameworks may not be suitable in application scenarios with large-scale data that require low latency, efficiency, and scalability [87], [137].…”

Section: ) Dl-based Edge Cachingmentioning

confidence: 99%

“…The process of sending streams of raw training data to a centralized server can result in several challenges, including slow response to real-time events in latency sensitive applications, excessive network communication resource consumption, increased network traffic, high energy consumption, and reduced privacy of training data [39], [43], [79], [87], [124], [137], [138]. Therefore, traditional DL frameworks may not be suitable in application scenarios with large-scale data that require low latency, efficiency, and scalability [87], [137]. Furthermore, it may be impractical to employ traditional DL frameworks in applications with privacy sensitive training data [39], [87].…”

Section: ) Dl-based Edge Cachingmentioning

confidence: 99%

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A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

Mutalemwa

Shin

2020

IEEE Access

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“…Deep reinforcement learning aims at maximizing the reward when the agent takes an action at the particular state. to the central machine learning processors [74][75][76][77][78]. Therefore federated learning is the decentralized machine learning approach which keeps the data at the generation point itself and then locally trained models are only transmitted to the central processor.…”

Section: Recommendation Via Q Learningmentioning

confidence: 99%

Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

Tapwal¹,

Gupta²,

Xin³

2020

Preprint

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<div>IoT devices (wireless sensors, actuators, computer devices) produce large volume and variety of data and the data</div><div>produced by the IoT devices are transient. In order to overcome the problem of traditional IoT architecture where</div><div>data is sent to the cloud for processing, an emerging technology known as fog computing is proposed recently.</div><div>Fog computing brings storage, computing and control near to the end devices. Fog computing complements the</div><div>cloud and provide services to the IoT devices. Hence, data used by the IoT devices must be cached at the fog nodes</div><div>in order to reduce the bandwidth utilization and latency. This chapter discusses the utility of data caching at the</div><div>fog nodes. Further, various machine learning techniques can be used to reduce the latency by caching the data</div><div>near to the IoT devices by predicting their future demands. Therefore, this chapter also discusses various machine</div><div>learning techniques that can be used to extract the accurate data and predict future requests of IoT devices.</div>

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

Cited by 482 publications

References 4 publications

Federated learning meets blockchain at 6G edge

Federated learning meets blockchain at 6G edge

A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

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