# Heterogeneous Data-Aware Federated Learning

arXiv: https://arxiv.org/abs/2011.06393 · HTML: https://mlsanity.com/paper/2011.06393/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications](https://mlsanity.com/paper/2104.03182/) (2021)
- [Federated Split BERT for Heterogeneous Text Classification](https://mlsanity.com/paper/2205.13299/) (2022)
- [FedHarmony: Unlearning Scanner Bias with Distributed Data](https://mlsanity.com/paper/2205.15970/) (2022)
- [Federated Ensemble-Directed Offline Reinforcement Learning](https://mlsanity.com/paper/2305.03097/) (2023)
- [On the Impact of Device and Behavioral Heterogeneity in Federated Learning](https://mlsanity.com/paper/2102.07500/) (2021)
- [A Federated Cox Model with Non-Proportional Hazards](https://mlsanity.com/paper/2207.05050/) (2022)
- [Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks](https://mlsanity.com/paper/2205.08347/) (2022)
- [Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning](https://mlsanity.com/paper/2311.17750/) (2023)
- [SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis](https://mlsanity.com/paper/2303.15965/) (2023)

## Abstract (arXiv)

Federated learning (FL) is an appealing concept to perform distributed training of Neural Networks (NN) while keeping data private. With the industrialization of the FL framework, we identify several problems hampering its successful deployment, such as presence of non i.i.d data, disjoint classes, signal multi-modality across datasets. In this work, we address these problems by proposing a novel method that not only (1) aggregates generic model parameters (e.g. a common set of task generic NN layers) on server (e.g. in traditional FL), but also (2) keeps a set of parameters (e.g, a set of task specific NN layer) specific to each client. We validate our method on the traditionally used public benchmarks (e.g., Femnist) as well as on our proprietary collected dataset (i.e., traffic classification). Results show the benefit of our method, with significant advantage on extreme cases.