Exploring parameter-efficient fine-tuning for improving communication efficiency in federated learning
Federated learning (FL) has emerged as a promising paradigm for enabling the
collaborative training of models without centralized access to the raw data on local devices …
collaborative training of models without centralized access to the raw data on local devices …
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning
Federated Learning (FL) is a distributed machine learning approach that enables training on
decentralized data while preserving privacy. However, FL systems often involve resource …
decentralized data while preserving privacy. However, FL systems often involve resource …
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
Y Ma, L Cheng, Y Wang, Z Zhong, X Xu… - arXiv preprint arXiv …, 2024 - arxiv.org
Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed
clients to collaboratively train models with a central server while keeping raw data locally. In …
clients to collaboratively train models with a central server while keeping raw data locally. In …
Dynamic attention-based communication-efficient federated learning
Federated learning (FL) offers a solution to train a global machine learning model while still
maintaining data privacy, without needing access to data stored locally at the clients …
maintaining data privacy, without needing access to data stored locally at the clients …
Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective
Federated Learning (FL) is a promising paradigm that offers significant advancements in
privacy-preserving, decentralized machine learning by enabling collaborative training of …
privacy-preserving, decentralized machine learning by enabling collaborative training of …
Conquering the communication constraints to enable large pre-trained models in federated learning
Federated learning (FL) has emerged as a promising paradigm for enabling the
collaborative training of models without centralized access to the raw data on local devices …
collaborative training of models without centralized access to the raw data on local devices …
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data
Federated learning (FL) is a promising approach that enables distributed clients to
collaboratively train a global model while preserving their data privacy. However, FL often …
collaboratively train a global model while preserving their data privacy. However, FL often …
On the convergence of hybrid federated learning with server-clients collaborative training
State-of-the-art federated learning (FL) paradigms utilize data collected and stored in
massively distributed clients to train a global machine learning (ML) model, in which local …
massively distributed clients to train a global machine learning (ML) model, in which local …
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
Federated learning (FL) has emerged as a prominent approach for collaborative training of
machine learning models across distributed clients while preserving data privacy. However …
machine learning models across distributed clients while preserving data privacy. However …
Fluid: Mitigating stragglers in federated learning using invariant dropout
Federated Learning (FL) allows machine learning models to train locally on individual
mobile devices, synchronizing model updates via a shared server. This approach …
mobile devices, synchronizing model updates via a shared server. This approach …