Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI

Distributed computing platforms typically assume the availability of reliable and dedicated connections among the processors. This work considers an alternative scenario, relevant for wireless data centers and federated learning, in which the distributed processors, operating on generally distinct coded data, are connected via shared wireless channels accessed via full-duplex transmission. The study accounts for both wireless and computing impairments, including interference, imperfect Channel State Information, and straggling processors, and it assumes a Map-Shuffle-Reduce coded computing paradigm. The total latency of the system, obtained as the sum of computing and communication delays, is studied for different shuffling strategies revealing the interplay between distributed computing, coding, and cooperative or coordinated transmission.Comment: Submitted for possible conference publicatio

Wireless for Machine Learning

As data generation increasingly takes place on devices without a wired connection, Machine Learning over wireless networks becomes critical. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support Distributed Machine Learning. This is creating the need for new wireless communication methods. In this survey, we give an exhaustive review of the state of the art wireless methods that are specifically designed to support Machine Learning services. Namely, over-the-air computation and radio resource allocation optimized for Machine Learning. In the over-the-air approach, multiple devices communicate simultaneously over the same time slot and frequency band to exploit the superposition property of wireless channels for gradient averaging over-the-air. In radio resource allocation optimized for Machine Learning, Active Learning metrics allow for data evaluation to greatly optimize the assignment of radio resources. This paper gives a comprehensive introduction to these methods, reviews the most important works, and highlights crucial open problems.Comment: Corrected typo in author name. From the incorrect Maitron to the correct Mairto

Securely Aggregated Coded Matrix Inversion

Coded computing is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. Federated learning is a decentralized model for training data distributed across client devices. In this work we propose approximating the inverse of an aggregated data matrix, where the data is generated by clients; similar to the federated learning paradigm, while also being resilient to stragglers. To do so, we propose a coded computing method based on gradient coding. We modify this method so that the coordinator does not access the local data at any point; while the clients access the aggregated matrix in order to complete their tasks. The network we consider is not centrally administrated, and the communications which take place are secure against potential eavesdroppers.Comment: arXiv admin note: substantial text overlap with arXiv:2207.0627

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective 5{th} generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The 3{rd} generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR