Abstract
Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs.
Highlights
Machine learning (ML) refers to computational approaches using experience to accomplish tasks, such as performance improvement or making accurate predictions
We summarized three major challenges, including incentive mechanisms for participants, privacy and security strategies for parameter communication, and aggregation algorithms for the generation of high-performance global models when applying Federated learning (FL) over software-defined networks (SDNs)
Since FL is a class of distributed ML approaches relying on participants to contribute their data, to train local models, and to share local models, we further suggested that estimation of the contributions, reputations, and fairness of participants encouraging more participants with high-quality data and resources is still the primary concern in current research
Summary
Machine learning (ML) refers to computational approaches using experience to accomplish tasks, such as performance improvement or making accurate predictions. The local controllers in the lower layer can be placed in the network edge to efficiently gather local data and local models, and the root controller in the upper layer can act as the central server to aggregate local models and produce a global model through standardized interfaces. Such SDNs with a distributed control plane allow FL approaches to be applied in a more scalable manner. We introduce three major challenges consisting of the incentive mechanism for participants, the privacy and security strategies for parameter communication, and aggregation algorithms for the generation of high performance global models.
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