# Client Adaptation improves Federated Learning with Simulated Non-IID Clients

arXiv: https://arxiv.org/abs/2007.04806 · HTML: https://mlsanity.com/paper/2007.04806/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection](https://mlsanity.com/paper/2201.00763/) (2022)
- [Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT](https://mlsanity.com/paper/2202.01512/) (2022)
- [LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning](https://mlsanity.com/paper/2107.12490/) (2021)

## Abstract (arXiv)

We present a federated learning approach for learning a client adaptable, robust model when data is non-identically and non-independently distributed (non-IID) across clients. By simulating heterogeneous clients, we show that adding learned client-specific conditioning improves model performance, and the approach is shown to work on balanced and imbalanced data set from both audio and image domains. The client adaptation is implemented by a conditional gated activation unit and is particularly beneficial when there are large differences between the data distribution for each client, a common scenario in federated learning.