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StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
Federated learning (FL) is a decentralized learning technique that enables participating devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) model without revealing their raw data to a third party.
Wireless Quantized Federated Learning: A Joint Computation and Communication Design
The impact of the quantization error on the convergence time is evaluated and the trade-off among model accuracy and timely execution is revealed.
Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends
Conventional machine learning techniques are conducted in a centralized manner.
Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigm
As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay for wireless federated learning (WFL), which is of paramount importance for the efficient integration of WFL in the sixth generation of wireless networks (6G).
