# On the Fusion Strategies for Federated Decision Making

arXiv: https://arxiv.org/abs/2303.06109 · HTML: https://mlsanity.com/paper/2303.06109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Context-Aware Crowd Counting](https://mlsanity.com/paper/1811.10452/) (2018)
- [Fast Convergence Rates for Distributed Non-Bayesian Learning](https://mlsanity.com/paper/1508.05161/) (2015)
- [Federated Learning under Importance Sampling](https://mlsanity.com/paper/2012.07383/) (2020)
- [Fusion of Probability Density Functions](https://mlsanity.com/paper/2202.11633/) (2022)
- [Social Learning and Distributed Hypothesis Testing](https://mlsanity.com/paper/1410.4307/) (2014)
- [Federated Analytics: A survey](https://mlsanity.com/paper/2302.01326/) (2023)
- [On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference](https://mlsanity.com/paper/2204.13741/) (2022)
- [Second Order Statistics Analysis and Comparison between Arithmetic and Geometric Average Fusion](https://mlsanity.com/paper/1901.08015/) (2019)
- [Learning from Heterogeneous Data Based on Social Interactions over Graphs](https://mlsanity.com/paper/2112.09483/) (2021)
- [A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization](https://mlsanity.com/paper/2206.12232/) (2022)
- [Multiobject fusion with minimum information loss](https://mlsanity.com/paper/1903.04239/) (2019)

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

- [Causal Influence in Federated Edge Inference](https://mlsanity.com/paper/2405.01260/) (2024)

## Abstract (arXiv)

We consider the problem of information aggregation in federated decision making, where a group of agents collaborate to infer the underlying state of nature without sharing their private data with the central processor or each other. We analyze the non-Bayesian social learning strategy in which agents incorporate their individual observations into their opinions (i.e., soft-decisions) with Bayes rule, and the central processor aggregates these opinions by arithmetic or geometric averaging. Building on our previous work, we establish that both pooling strategies result in asymptotic normality characterization of the system, which, for instance, can be utilized to derive approximate expressions for the error probability. We verify the theoretical findings with simulations and compare both strategies.