# Resource-Constrained Federated Learning with Heterogeneous Labels and Models

arXiv: https://arxiv.org/abs/2011.03206 · HTML: https://mlsanity.com/paper/2011.03206/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Zero-Shot Federated Learning with New Classes for Audio Classification](https://mlsanity.com/paper/2106.10019/) (2021)
- [Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives](https://mlsanity.com/paper/2307.02140/) (2023)
- [Robust Convergence in Federated Learning through Label-wise Clustering](https://mlsanity.com/paper/2112.14244/) (2021)

## Abstract (arXiv)

Various IoT applications demand resource-constrained machine learning mechanisms for different applications such as pervasive healthcare, activity monitoring, speech recognition, real-time computer vision, etc. This necessitates us to leverage information from multiple devices with few communication overheads. Federated Learning proves to be an extremely viable option for distributed and collaborative machine learning. Particularly, on-device federated learning is an active area of research, however, there are a variety of challenges in addressing statistical (non-IID data) and model heterogeneities. In addition, in this paper we explore a new challenge of interest -- to handle label heterogeneities in federated learning. To this end, we propose a framework with simple α-weighted federated aggregation of scores which leverages overlapping information gain across labels, while saving bandwidth costs in the process. Empirical evaluation on Animals-10 dataset (with 4 labels for effective elucidation of results) indicates an average deterministic accuracy increase of at least ~16.7%. We also demonstrate the on-device capabilities of our proposed framework by experimenting with federated learning and inference across different iterations on a Raspberry Pi 2, a single-board computing platform.