Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems

Abstract

The communication and networking field is hungry for machine learning decision-making solutions to replace the traditional model-driven approaches that proved to be not rich enough for seizing the ever-growing complexity and heterogeneity of the modern systems in the field. Traditional machine learning solutions assume the existence of (cloud-based) central entities that are in charge of processing the data. Nonetheless, the difficulty of accessing private data, together with the high cost of transmitting raw data to the central entity gave rise to a decentralized machine learning approach called Federated Learning. The main idea of federated learning is to perform an on-device collaborative training of a single machine learning model without having to share the raw training data with any third-party entity. Although few survey articles on federated learning already exist in the literature, the motivation of this survey stems from three essential observations. The first one is the lack of a fine-grained multi-level classification of the federated learning literature, where the existing surveys base their classification on only one criterion or aspect. The second observation is that the existing surveys focus only on some common challenges, but disregard other essential aspects such as reliable client selection, resource management and training service pricing. The third observation is the lack of explicit and straightforward directives for researchers to help them design future federated learning solutions that overcome the state-of-the-art research gaps. To address these points, we first provide a comprehensive tutorial on federated learning and its associated concepts, technologies and learning approaches. We then survey and highlight the applications and future directions of federated learning in the domain of communication and networking. Thereafter, we design a three-level classification scheme that first categorizes the federated learning literature based on the high-level challenge that they tackle. Then, we classify each high-level challenge into a set of specific low-level challenges to foster a better understanding of the topic. Finally, we provide, within each low-level challenge, a fine-grained classification based on the technique used to address this particular challenge. For each category of high-level challenges, we provide a set of desirable criteria and future research directions that are aimed to help the research community design innovative and efficient future solutions. To the best of our knowledge, our survey is the most comprehensive in terms of challenges and techniques it covers and the most fine-grained in terms of the multi-level classification scheme it presents.

Federated Learning in Edge Computing: A Systematic Survey

“…The authors of [101] provided a review of FL technologies within the biomedical space and the challenges. Moreover, the authors of [102] presented a comprehensive tutorial on FL in the domain of communication and networking. However, none of the publications in [50,[90][91][92][93][94][95][96][97][98][99][100][101][102] adopted the SLR methodology.…”

“…Moreover, the authors of [102] presented a comprehensive tutorial on FL in the domain of communication and networking. However, none of the publications in [50,[90][91][92][93][94][95][96][97][98][99][100][101][102] adopted the SLR methodology. Moreover, the authors of [50,90,91,94,102] did not take into account the challenges of FL implementation in the EC paradigm.…”

“…However, none of the publications in [50,[90][91][92][93][94][95][96][97][98][99][100][101][102] adopted the SLR methodology. Moreover, the authors of [50,90,91,94,102] did not take into account the challenges of FL implementation in the EC paradigm. Furthermore, the existing literature except [7,94] did not analyze the hardware requirements in implementing FL in the EC environment.…”

Federated Deep Learning for Zero-Day Botnet Attack Detection in IoT-Edge Devices

“…However, the authors did not consider zero-day cyber-attack vulnerabilities in edge IoT devices. In real-life scenario, the training data on each IoT edge device is expected to have a unique statistical distribution depending on the usage pattern [53]. Therefore, the sizes of local training data in IoT edge devices should vary.…”

Fast and Fair Computation Offloading Management in a Swarm of Drones Using a Rating-Based Federated Learning Approach

“…The processing capacity and power of drones is lower than that of servers, which means that providing appropriate and optimal methods for resource management (orchestration) in a swarm of drones is highly needed. A distributed resource orchestration is preferable to a centralized one due to offloading some tasks to other devices [12]. In fact, a distributed architecture decreases the number of delayed and dropped tasks.…”