Sparse random networks for communication-efficient federated learning
One main challenge in federated learning is the large communication cost of exchanging
weight updates from clients to the server at each round. While prior work has made great …
weight updates from clients to the server at each round. While prior work has made great …
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
Federated learning is a promising distributed machine learning paradigm that can effectively
exploit large-scale data without exposing users' privacy. However, it may incur significant …
exploit large-scale data without exposing users' privacy. However, it may incur significant …
Masked Random Noise for Communication Efficient Federaetd Learning
Federated learning is a promising distributed training paradigm that effectively safeguards
data privacy. However, it may involve significant communication costs, which hinders …
data privacy. However, it may involve significant communication costs, which hinders …
FedDSE: Distribution-aware Sub-model Extraction for Federated Learning over Resource-constrained Devices
Sub-model extraction based federated learning has emerged as a popular strategy for
training models on resource-constrained devices. However, existing methods treat all clients …
training models on resource-constrained devices. However, existing methods treat all clients …
Adaptive Compression in Federated Learning via Side Information
The high communication cost of sending model updates from the clients to the server is a
significant bottleneck for scalable federated learning (FL). Among existing approaches, state …
significant bottleneck for scalable federated learning (FL). Among existing approaches, state …
[HTML][HTML] Once-for-All Federated Learning: Learning from and deploying to heterogeneous clients
Federated learning (FL) enables multiple client devices to train a single machine learning
model collaboratively. As FL often involves various smart devices, it is important to adapt the …
model collaboratively. As FL often involves various smart devices, it is important to adapt the …
Efficient federated random subnetwork training
One main challenge in federated learning is the large communication cost of exchanging
weight updates from clients to the server at each round. While prior work has made great …
weight updates from clients to the server at each round. While prior work has made great …
On the Role of Information in Distributed Learning
F Pase - 2024 - research.unipd.it
Today, most data consumed by machine learning algorithms is generated by the enormous
amount of sensors and embedded devices like smartphones, cars, drones, which are …
amount of sensors and embedded devices like smartphones, cars, drones, which are …
FedSNIP: Método baseado em Poda de Modelo de Etapa Unica para Comunicaçao Eficiente em Aprendizado Federado
No âmbito do Aprendizado Federado (AF), uma abordagem colaborativa, porém
descentralizada, para a aprendizagem de máquina, a eficiência da comunicação é uma …
descentralizada, para a aprendizagem de máquina, a eficiência da comunicação é uma …
Leveraging Side Information for Communication-Efficient Federated Learning
The high communication cost of sending model updates from the clients to the server is a
significant bottleneck for scalable federated learning (FL). Among existing approaches, state …
significant bottleneck for scalable federated learning (FL). Among existing approaches, state …