7 papers
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…
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization
Mrinmay Sen, Chalavadi Krishna Mohan
Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models tailored to their individual objectives, addressing the challenge of model genera…
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…