# TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering

arXiv: https://arxiv.org/abs/2409.10392 · HTML: https://mlsanity.com/paper/2409.10392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 46 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2111.14213/) (2021)
- [Multi-Center Federated Learning: Clients Clustering for Better Personalization](https://mlsanity.com/paper/2108.08647/) (2021)
- [Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning](https://mlsanity.com/paper/2105.05883/) (2021)
- [Robust Federated Learning in a Heterogeneous Environment](https://mlsanity.com/paper/1906.06629/) (2019)
- [The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic](https://mlsanity.com/paper/1804.01508/) (2018)
- [When Machine Learning Meets Privacy: A Survey and Outlook](https://mlsanity.com/paper/2011.11819/) (2020)
- [FedSoft: Soft Clustered Federated Learning with Proximal Local Updating](https://mlsanity.com/paper/2112.06053/) (2021)
- [The Convolutional Tsetlin Machine](https://mlsanity.com/paper/1905.09688/) (2019)
- [Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions](https://mlsanity.com/paper/2012.06810/) (2020)
- [On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators](https://mlsanity.com/paper/2007.14268/) (2020)
- [FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution](https://mlsanity.com/paper/2208.09754/) (2022)
- [A Relational Tsetlin Machine with Applications to Natural Language Understanding](https://mlsanity.com/paper/2102.10952/) (2021)
- [Clustered Federated Learning Architecture for Network Anomaly Detection in Large Scale Heterogeneous IoT Networks](https://mlsanity.com/paper/2303.15986/) (2023)

## Built on by (most cited first, 1 of 1)

- [Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference](https://mlsanity.com/paper/2607.20124/) (2026)

## Abstract (arXiv)

The world of Machine Learning (ML) has witnessed rapid changes in terms of new models and ways to process users data. The majority of work that has been done is focused on Deep Learning (DL) based approaches. However, with the emergence of new algorithms such as the Tsetlin Machine (TM) algorithm, there is growing interest in exploring alternative approaches that may offer unique advantages in certain domains or applications. One of these domains is Federated Learning (FL), in which users privacy is of utmost importance. Due to its novelty, FL has seen a surge in the incorporation of personalization techniques to enhance model accuracy while maintaining user privacy under personalized conditions. In this work, we propose a novel approach called TPFL: Tsetlin-Personalized Federated Learning, in which models are grouped into clusters based on their confidence towards a specific class. In this way, clustering can benefit from two key advantages. Firstly, clients share only what they are confident about, resulting in the elimination of wrongful weight aggregation among clients whose data for a specific class may have not been enough during the training. This phenomenon is prevalent when the data are non-Independent and Identically Distributed (non-IID). Secondly, by sharing only weights towards a specific class, communication cost is substantially reduced, making TPLF efficient in terms of both accuracy and communication cost. The TPFL results were compared with 6 other baseline methods; namely FedAvg, FedProx, FLIS DC, FLIS HC, IFCA and FedTM. The results demonstrated that TPFL performance better than baseline methods with 98.94% accuracy on MNIST, 98.52% accuracy on FashionMNIST and 91.16% accuracy on FEMNIST dataset.