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Found 6 papers, 3 papers with code
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation
Federated Learning (FL) is a promising technique for the collaborative training of deep neural networks across multiple devices while preserving data privacy.
FedCode: Communication-Efficient Federated Learning via Transferring Codebooks
To ensure a smooth learning curve and proper calibration of clusters between the server and the clients, FedCode periodically transfers model weights after multiple rounds of solely communicating codebooks.
Intelligent Blockage Recognition using Cellular mmWave Beamforming Data: Feasibility Study
Joint Communication and Sensing (JCAS) is envisioned for 6G cellular networks, where sensing the operation environment, especially in presence of humans, is as important as the high-speed wireless connectivity.
Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets, where the labeling effort is entrusted to the clients.
Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning
To the best of our knowledge, this is the first federated SER approach, which utilizes self-training learning in conjunction with federated learning to exploit both labeled and unlabeled on-device data.
Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks
The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication.
