# Leveraging Learning Metrics for Improved Federated Learning

arXiv: https://arxiv.org/abs/2309.00257 · HTML: https://mlsanity.com/paper/2309.00257/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 1 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Free-rider Attacks on Model Aggregation in Federated Learning](https://mlsanity.com/paper/2006.11901/) (2020)
- [Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating](https://mlsanity.com/paper/1910.08234/) (2019)
- [A Closer Look at Codistillation for Distributed Training](https://mlsanity.com/paper/2010.02838/) (2020)
- [Precision-Weighted Federated Learning](https://mlsanity.com/paper/2107.09627/) (2021)
- [CONet: Channel Optimization for Convolutional Neural Networks](https://mlsanity.com/paper/2108.06822/) (2021)
- [In Search of Probeable Generalization Measures](https://mlsanity.com/paper/2110.12259/) (2021)
- [Exploiting Explainable Metrics for Augmented SGD](https://mlsanity.com/paper/2203.16723/) (2022)

## Abstract (arXiv)

Currently in the federated setting, no learning schemes leverage the emerging research of explainable artificial intelligence (XAI) in particular the novel learning metrics that help determine how well a model is learning. One of these novel learning metrics is termed `Effective Rank' (ER) which measures the Shannon Entropy of the singular values of a matrix, thus enabling a metric determining how well a layer is mapping. By joining federated learning and the learning metric, effective rank, this work will (1) give the first federated learning metric aggregation method (2) show that effective rank is well-suited to federated problems by out-performing baseline Federated Averaging citekonevcny2016federated and (3) develop a novel weight-aggregation scheme relying on effective rank.