Mrinmay Sen

Orcid: 0000-0001-9550-7709

According to our database1, Mrinmay Sen authored at least 16 papers between 2023 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
DP-FEDSOFIM: Differentially Private Federated Stochastic Optimization using Regularized Fisher Information Matrix.
CoRR, January, 2026

2025
FedDPC : Handling Data Heterogeneity and Partial Client Participation in Federated Learning.
CoRR, December, 2025

FedDAF: Federated Domain Adaptation Using Model Functional Distance.
CoRR, September, 2025

pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization.
CoRR, June, 2025

Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review.
CoRR, June, 2025

Accelerated Training of Federated Learning via Second-Order Methods.
CoRR, May, 2025

2024
HateTinyLLM : Hate Speech Detection Using Tiny Large Language Models.
CoRR, 2024

FAGH: Accelerating Federated Learning with Approximated Global Hessian.
CoRR, 2024

SOFIM: Stochastic Optimization Using Regularized Fisher Information Matrix.
Proceedings of the International Joint Conference on Neural Networks, 2024

2023
FONN: Federated Optimization with Nys-Newton.
Proceedings of the IEEE Region 10 Conference, 2023

Federated Optimization with Linear-Time Approximated Hessian Diagonal.
Proceedings of the Pattern Recognition and Machine Intelligence, 2023

Handling Data Heterogeneity in Federated Learning with Global Data Distribution.
Proceedings of the 3rd International Conference on Image Processing and Vision Engineering, 2023

FReNG: Federated Optimization by Using Regularized Natural Gradient Descent.
Proceedings of the International Conference on Machine Learning and Applications, 2023

Nys-FL: A communication efficient Federated learning with Nyström approximated Global Newton direction.
Proceedings of the IEEE International Conference on High Performance Computing & Communications, 2023

NOAH: Newton Method of Optimization with Approximated Hessian.
Proceedings of the IEEE International Conference on High Performance Computing & Communications, 2023

FopLAHD: Federated Optimization Using Locally Approximated Hessian Diagonal.
Proceedings of the Big Data and Artificial Intelligence - 11th International Conference, 2023


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