# Federated Adaptation of Reservoirs via Intrinsic Plasticity

arXiv: https://arxiv.org/abs/2206.11087 · HTML: https://mlsanity.com/paper/2206.11087/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 3 of 3)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence](https://mlsanity.com/paper/2107.06543/) (2021)
- [AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving](https://mlsanity.com/paper/2202.01645/) (2022)

## Abstract (arXiv)

We propose a novel algorithm for performing federated learning with Echo State Networks (ESNs) in a client-server scenario. In particular, our proposal focuses on the adaptation of reservoirs by combining Intrinsic Plasticity with Federated Averaging. The former is a gradient-based method for adapting the reservoir's non-linearity in a local and unsupervised manner, while the latter provides the framework for learning in the federated scenario. We evaluate our approach on real-world datasets from human monitoring, in comparison with the previous approach for federated ESNs existing in literature. Results show that adapting the reservoir with our algorithm provides a significant improvement on the performance of the global model.