# Optimally Improving Cooperative Learning in a Social Setting

arXiv: https://arxiv.org/abs/2405.20808 · HTML: https://mlsanity.com/paper/2405.20808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [GEMSEC: Graph Embedding with Self Clustering](https://mlsanity.com/paper/1802.03997/) (2018)
- [A Survey on Gradient Inversion: Attacks, Defenses and Future Directions](https://mlsanity.com/paper/2206.07284/) (2022)
- [Gossip training for deep learning](https://mlsanity.com/paper/1611.09726/) (2016)
- [Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions](https://mlsanity.com/paper/1606.02421/) (2016)
- [Minimizing Polarization and Disagreement in Social Networks](https://mlsanity.com/paper/1712.09948/) (2017)
- [Polarization in Geometric Opinion Dynamics](https://mlsanity.com/paper/2106.12459/) (2021)
- [Balancing Information Exposure in Social Networks](https://mlsanity.com/paper/1709.01491/) (2017)
- [A Geometric Model of Opinion Polarization](https://mlsanity.com/paper/1910.05274/) (2019)
- [RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions](https://mlsanity.com/paper/2101.04751/) (2021)
- [Opinion Dynamics with Varying Susceptibility to Persuasion](https://mlsanity.com/paper/1801.07863/) (2018)
- [Bayesian Learning without Recall](https://mlsanity.com/paper/1601.06103/) (2016)
- [Recommending links through influence maximization](https://mlsanity.com/paper/1706.04368/) (2017)
- [Reasoning in Bayesian Opinion Exchange Networks Is PSPACE-Hard](https://mlsanity.com/paper/1809.01077/) (2018)
- [Seeding with Costly Network Information](https://mlsanity.com/paper/1905.04325/) (2019)
- [Rebalancing Social Feed to Minimize Polarization and Disagreement](https://mlsanity.com/paper/2308.14486/) (2023)
- [On social networks that support learning](https://mlsanity.com/paper/2011.05255/) (2020)
- [Minimizing Hitting Time between Disparate Groups with Shortcut Edges](https://mlsanity.com/paper/2306.03571/) (2023)

## Abstract (arXiv)

We consider a cooperative learning scenario where a collection of networked agents with individually owned classifiers dynamically update their predictions, for the same classification task, through communication or observations of each other's predictions. Clearly if highly influential vertices use erroneous classifiers, there will be a negative effect on the accuracy of all the agents in the network. We ask the following question: how can we optimally fix the prediction of a few classifiers so as maximize the overall accuracy in the entire network. To this end we consider an aggregate and an egalitarian objective function. We show a polynomial time algorithm for optimizing the aggregate objective function, and show that optimizing the egalitarian objective function is NP-hard. Furthermore, we develop approximation algorithms for the egalitarian improvement. The performance of all of our algorithms are guaranteed by mathematical analysis and backed by experiments on synthetic and real data.