6 papers · 1 filter
FedDPC : Handling Data Heterogeneity and Partial Client Participation in Federated Learning
Mrinmay Sen, Subhrajit Nag
Data heterogeneity is a significant challenge in modern federated learning (FL) as it creates variance in local model updates, causing the aggregated global model to shift away fro…
FedDAF: Federated Domain Adaptation Using Model Functional Distance
Mrinmay Sen, Sidhant Nair, C Krishna Mohan
Federated Domain Adaptation (FDA) improves model performance at a target client by collaborating with source clients while preserving data privacy. FDA faces two key challenges: do…
Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review
Mrinmay Sen, Shruti Aparna, Rohit Agarwal +1
Federated Learning (FL) is a learning mechanism that falls under the distributed training umbrella, which collaboratively trains a shared global model without disclosing the raw da…
Accelerated Training of Federated Learning via Second-Order Methods
Mrinmay Sen, Sidhant R Nair, C Krishna Mohan
This paper explores second-order optimization methods in Federated Learning (FL), addressing the critical challenges of slow convergence and the excessive communication rounds requ…
SOFIM: Stochastic Optimization Using Regularized Fisher Information Matrix
Mrinmay Sen, A. K. Qin, Gayathri C +3
This paper introduces a new stochastic optimization method based on the regularized Fisher information matrix (FIM), named SOFIM, which can efficiently utilize the FIM to approxima…
FAGH: Accelerating Federated Learning with Approximated Global Hessian
Mrinmay Sen, A. K. Qin, Krishna Mohan C
In federated learning (FL), the significant communication overhead due to the slow convergence speed of training the global model poses a great challenge. Specifically, a large num…