Conventional online multi-task learning algorithms suffer from two critical limitations: 1) Heavy communication caused by delivering high velocity of sequential data to a central machine; 2) Expensive runtime complexity for building task relatedness. To address these issues, in this paper we consider ...
Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others. This paper mainly sorts out FLs based on machi...
A multitask deep neural network in federated learning (MT-DNN-FL) is presented in [33] to perform network anomaly detection tasks. It offers simultaneous execution of tasks like network anomaly detection, VPN (Tor) traffic recognition, and traffic classification, which provides more information to...
Though multitask learning is not needed for 5G [119], it is needed in 6G-enabled MEC [120]. A lot of intensive work on multitasking in 5G wireless communications is required because of the limited number of publications found in the literature. 5. Challenges and Future Perspective Despite ...
federated search engine, in the form of a web and mobile app, was developed for this research’s purposes. This platform was used to monitor actual user behavior during a six-month period. Quantitative data related to the platform’s usage were collected and analyzed in order to provide a ...
Mehrotra et al.'s findings on the affinity of various topics to be better suited for multitask or single-task searches indicated the existence of inherent char- acteristics of the various topics themselves and how they may influence user behavior [17]. In Mehrotra's research, the arts ...