3 papers
cs.LG2026
FedLoRD: Personalized Federated Learning based on Low-Rank Structures of Model Parameter Differences
Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi +1
We propose FedLoRD, a personalized federated learning (PFL) method based on the low-rank structure of the differences between the global and personalized model parameters. PFL is a…
cs.LG2025
Joint Graph Estimation and Signal Restoration for Robust Federated Learning
Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi +1
We propose a robust aggregation method for model parameters in federated learning (FL) under noisy communications. FL is a distributed machine learning paradigm in which a central…
eess.SP2024
Time-Varying Graph Signal Estimation among Multiple Sub-Networks
Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi +1
This paper presents an estimation method for time-varying graph signals among multiple sub-networks. In many sensor networks, signals observed are associated with nodes (i.e., sens…