Fedl2p: Federated learning to personalize
Federated learning (FL) research has made progress in developing algorithms for
distributed learning of global models, as well as algorithms for local personalization of those …
distributed learning of global models, as well as algorithms for local personalization of those …
Spectral co-distillation for personalized federated learning
Personalized federated learning (PFL) has been widely investigated to address the
challenge of data heterogeneity, especially when a single generic model is inadequate in …
challenge of data heterogeneity, especially when a single generic model is inadequate in …
A Review of Federated Learning Methods in Heterogeneous scenarios
Federated learning emerges as a solution to the dilemma of data silos while safeguarding
data privacy, particularly relevant in the consumer electronics sector where user data privacy …
data privacy, particularly relevant in the consumer electronics sector where user data privacy …
Bold but cautious: Unlocking the potential of personalized federated learning through cautiously aggressive collaboration
Personalized federated learning (PFL) reduces the impact of non-independent and
identically distributed (non-IID) data among clients by allowing each client to train a …
identically distributed (non-IID) data among clients by allowing each client to train a …
ZooPFL: Exploring black-box foundation models for personalized federated learning
When personalized federated learning (FL) meets large foundation models, new challenges
arise from various limitations in resources. In addition to typical limitations such as data …
arise from various limitations in resources. In addition to typical limitations such as data …
FedHCA2: Towards Hetero-Client Federated Multi-Task Learning
Federated Learning (FL) enables joint training across distributed clients using their local
data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks …
data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks …
A Content-based Viewport Prediction Framework for 360° Video Using Personalized Federated Learning and Fusion Techniques
M Setayesh, VWS Wong - 2023 IEEE International Conference …, 2023 - ieeexplore.ieee.org
Viewport prediction is a key enabler for 360° video streaming over wireless networks. To
improve the prediction accuracy, a common approach is to use a content-based viewport …
improve the prediction accuracy, a common approach is to use a content-based viewport …
Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360-Degree Video Streaming
M Setayesh, VWS Wong - arXiv preprint arXiv:2401.13114, 2024 - arxiv.org
360-degree videos require significant bandwidth to provide an immersive viewing
experience. Wireless systems using terahertz (THz) frequency band can meet this high data …
experience. Wireless systems using terahertz (THz) frequency band can meet this high data …
Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs
For modern artificial intelligence (AI) applications such as large language models (LLMs),
the training paradigm has recently shifted to pre-training followed by fine-tuning …
the training paradigm has recently shifted to pre-training followed by fine-tuning …
Learn What You Need in Personalized Federated Learning
Personalized federated learning aims to address data heterogeneity across local clients in
federated learning. However, current methods blindly incorporate either full model …
federated learning. However, current methods blindly incorporate either full model …