Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
Topology-aware federated learning in edge computing: A comprehensive survey
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …
distributed machine learning systems to be deployed at the edge. With its simple yet …
Efficient model personalization in federated learning via client-specific prompt generation
Federated learning (FL) emerges as a decentralized learning framework which trains
models from multiple distributed clients without sharing their data to preserve privacy …
models from multiple distributed clients without sharing their data to preserve privacy …
Fs-real: Towards real-world cross-device federated learning
Federated Learning (FL) aims to train high-quality models in collaboration with distributed
clients while not uploading their local data, which attracts increasing attention in both …
clients while not uploading their local data, which attracts increasing attention in both …
Revisiting personalized federated learning: Robustness against backdoor attacks
In this work, besides improving prediction accuracy, we study whether personalization could
bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks …
bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks …
Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes
Pre-trained large language models (LLMs) require fine-tuning to improve their
responsiveness to natural language instructions. Federated learning (FL) offers a way to …
responsiveness to natural language instructions. Federated learning (FL) offers a way to …
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 …
On the Convergence of Zeroth-Order Federated Tuning in Large Language Models
The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering
in a new era in privacy-preserving natural language processing. However, the intensive …
in a new era in privacy-preserving natural language processing. However, the intensive …
Decentralized Directed Collaboration for Personalized Federated Learning
Abstract Personalized Federated Learning (PFL) is proposed to find the greatest
personalized models for each client. To avoid the central failure and communication …
personalized models for each client. To avoid the central failure and communication …
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 …