Federated learning for computationally constrained heterogeneous devices: A survey
With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …
field, offloading training of neural networks (NNs) to a central server becomes more and …
FedBERT: When Federated Learning Meets Pre-training
The fast growth of pre-trained models (PTMs) has brought natural language processing to a
new era, which has become a dominant technique for various natural language processing …
new era, which has become a dominant technique for various natural language processing …
Limitations and future aspects of communication costs in federated learning: A survey
This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …
modern distributed systems. FL is an emerging distributed machine learning technique that …
A survey on heterogeneous federated learning
Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …
the isolated data silos by cooperatively training models among organizations without …
Resource-adaptive federated learning with all-in-one neural composition
Abstract Conventional Federated Learning (FL) systems inherently assume a uniform
processing capacity among clients for deployed models. However, diverse client hardware …
processing capacity among clients for deployed models. However, diverse client hardware …
Layer-wise adaptive model aggregation for scalable federated learning
Abstract In Federated Learning (FL), a common approach for aggregating local solutions
across clients is periodic full model averaging. It is, however, known that different layers of …
across clients is periodic full model averaging. It is, however, known that different layers of …
Communication-efficient personalized federated meta-learning in edge networks
Due to the privacy breach risks and data aggregation of traditional centralized machine
learning (ML) approaches, applications, data and computing power are being pushed from …
learning (ML) approaches, applications, data and computing power are being pushed from …
Towards understanding ensemble distillation in federated learning
Federated Learning (FL) is a collaborative machine learning paradigm for data privacy
preservation. Recently, a knowledge distillation (KD) based information sharing approach in …
preservation. Recently, a knowledge distillation (KD) based information sharing approach in …
Federated learning of large models at the edge via principal sub-model training
Limited compute, memory, and communication capabilities of edge users create a significant
bottleneck for federated learning (FL) of large models. Current literature typically tackles the …
bottleneck for federated learning (FL) of large models. Current literature typically tackles the …
FedPE: Adaptive Model Pruning-Expanding for Federated Learning on Mobile Devices
Recently, federated learning (FL) as a new learning paradigm allows multi-party to
collaboratively train a shared global model with privacy protection. However, vanilla FL …
collaboratively train a shared global model with privacy protection. However, vanilla FL …