FLOM: Toward Efficient Task Processing in Big Data with Federated Learning

• Published in: Security and communication networks Vol. 2022; pp. 1 - 16
• Main Authors: Wu, Chunyi, Li, Ya
• Format: Journal Article
• Language: English
• Published: London Hindawi 27.01.2022; Hindawi Limited
• Subjects: Artificial intelligence; Big Data; Blockchain; Cellular telephones; Classification; Computer centers; Data management; Data processing; Deep learning; Distributed processing; Edge computing; Federated learning; Information sharing; Internet of Things; Load balancing; Machine learning; Neural networks; Optimization; Privacy; Substrates; User requirements
• ISSN: 1939-0114; 1939-0122
• DOI: 10.1155/2022/5277362

With the diversification and individuation of user requirements as well as the rapid development of computing technology, the large-scale tasks processing for big data in edge computing environment has become a research focus nowadays. Many recent efforts for task processing are designed and implemented based on some traditional protocols and optimization methods. Therefore, it is more difficult to explore the task allocation strategy that maximizes the overall system revenue from the perspective of global load balancing. In order to overcome this problem, a large-scale tasks processing approach called Federated Learning based Optimization Methodology (FLOM) for large-scale tasks processing was presented to achieve accurate task classification and overall load balancing while satisfying task allocation requirements. FLOM performs the data aggregation and establishes the personalized models by federated learning. The deep network model is designed for deep feature learning of task requests and hosts in the substrate network. The experimental results show the capability of FLOM in terms of large-scale task classification as well as allocation.