Hierarchical split federated learning: Convergence analysis and system optimization
As AI models expand in size, it has become increasingly challenging to deploy federated
learning (FL) on resource-constrained edge devices. To tackle this issue, split federated …
learning (FL) on resource-constrained edge devices. To tackle this issue, split federated …
Energy-Efficient Hierarchical Collaborative Learning over LEO Satellite Constellations
The hierarchical collaborative learning within Low Earth Orbit (LEO) satellite constellations,
termed LEO-HCL, is gaining increasing popularity by integrating intra-orbit Inter-Satellite …
termed LEO-HCL, is gaining increasing popularity by integrating intra-orbit Inter-Satellite …
Efficient inter-datacenter ALLReduce with multiple trees
In this paper, we look into the problem of achieving efficient inter-datacenter AllReduce
operations for geo-distributed machine learning (Geo-DML). Compared with intra-datacenter …
operations for geo-distributed machine learning (Geo-DML). Compared with intra-datacenter …
Releasing the power of in-network aggregation with aggregator-aware routing optimization
By offloading partial of the aggregation computation from the logical central parameter
servers to network devices like programmable switches, In-Network Aggregation (INA) is a …
servers to network devices like programmable switches, In-Network Aggregation (INA) is a …
Optimal communication and key rate region for hierarchical secure aggregation with user collusion
Secure aggregation is concerned with the task of securely uploading the inputs of multiple
users to an aggregation server without letting the server know the inputs beyond their …
users to an aggregation server without letting the server know the inputs beyond their …
Resource-aware Personalized Federated Learning Based on Reinforcement Learning
T Wu, X Li, P Gao, W Yu, L Xin… - IEEE Communications …, 2024 - ieeexplore.ieee.org
Federated learning is an effective solution to protect data privacy, but the efficiency and
performance of the entire federated system are challenging to balance due to the …
performance of the entire federated system are challenging to balance due to the …
Capacity of Hierarchical Secure Coded Gradient Aggregation with Straggling Communication Links
The growing privacy concerns in distributed learning have led to the widespread adoption of
secure aggregation techniques in distributed machine learning systems, such as federated …
secure aggregation techniques in distributed machine learning systems, such as federated …