# A Robust Federated Learning Framework for Undependable Devices at Scale

arXiv: https://arxiv.org/abs/2412.19991 · HTML: https://mlsanity.com/paper/2412.19991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 72 references that are in the map.
- First posted on arXiv 28 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (2021)
- [Federated Learning With Quantized Global Model Updates](https://mlsanity.com/paper/2006.10672/) (2020)
- [Communication Efficiency in Federated Learning: Achievements and Challenges](https://mlsanity.com/paper/2107.10996/) (2021)
- [FEDZIP: A Compression Framework for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2102.01593/) (2021)
- [On-Device Recommender Systems: A Comprehensive Survey](https://mlsanity.com/paper/2401.11441/) (2024)
- [AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation](https://mlsanity.com/paper/2205.13797/) (2022)

## Abstract (arXiv)

In a federated learning (FL) system, many devices, such as smartphones, are often undependable (e.g., frequently disconnected from WiFi) during training. Existing FL frameworks always assume a dependable environment and exclude undependable devices from training, leading to poor model performance and resource wastage. In this paper, we propose FLUDE to effectively deal with undependable environments. First, FLUDE assesses the dependability of devices based on the probability distribution of their historical behaviors (e.g., the likelihood of successfully completing training). Based on this assessment, FLUDE adaptively selects devices with high dependability for training. To mitigate resource wastage during the training phase, FLUDE maintains a model cache on each device, aiming to preserve the latest training state for later use in case local training on an undependable device is interrupted. Moreover, FLUDE proposes a staleness-aware strategy to judiciously distribute the global model to a subset of devices, thus significantly reducing resource wastage while maintaining model performance. We have implemented FLUDE on two physical platforms with 120 smartphones and NVIDIA Jetson devices. Extensive experimental results demonstrate that FLUDE can effectively improve model performance and resource efficiency of FL training in undependable environments.