Federated continual learning via knowledge fusion: A survey
Data privacy and silos are nontrivial and greatly challenging in many real-world
applications. Federated learning is a decentralized approach to training models across …
applications. Federated learning is a decentralized approach to training models across …
Framework: Clustering-Driven Approach for Base Station Parameter Optimization and Automation (CeDA-BatOp)
SP Jayakumar, A Conte - 2024 IEEE 21st Consumer …, 2024 - ieeexplore.ieee.org
The growing demand for fast and reliable wireless services has led to the deployment of
more base stations, which has made manual optimization of base station parameters more …
more base stations, which has made manual optimization of base station parameters more …
Joint participation incentive and network pricing design for federated learning
Federated learning protects users' data privacy though sharing users' local model
parameters (instead of raw data) with a server. However, when massive users train a large …
parameters (instead of raw data) with a server. However, when massive users train a large …
CeDA-BatOp 2.0: Enhanced Framework for Base Station Parameter Optimization and Automation with Joint Optimization, Controlled Drift Analysis and Pseudo …
SP Jayakumar, A Conte - 2023 9th International Conference on …, 2023 - ieeexplore.ieee.org
In the ever-evolving landscape of cellular networks, the pursuit of optimal network
performance remains a constant endeavor. This paper introduces CeDA-BatOp 2.0, an …
performance remains a constant endeavor. This paper introduces CeDA-BatOp 2.0, an …