# New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design

arXiv: https://arxiv.org/abs/2008.10805 · HTML: https://mlsanity.com/paper/2008.10805/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

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

- [CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization](https://mlsanity.com/paper/2203.05468/) (2022)
- [Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client](https://mlsanity.com/paper/2405.08183/) (2024)
- [Extra Clients at No Extra Cost: Overcome Data Heterogeneity in Federated Learning with Filter Decomposition](https://mlsanity.com/paper/2503.08652/) (2025)

## Abstract (arXiv)

In this paper, we first highlight three major challenges to large-scale adoption of deep learning at the edge: (i) Hardware-constrained IoT devices, (ii) Data security and privacy in the IoT era, and (iii) Lack of network-aware deep learning algorithms for distributed inference across multiple IoT devices. We then provide a unified view targeting three research directions that naturally emerge from the above challenges: (1) Federated learning for training deep networks, (2) Data-independent deployment of learning algorithms, and (3) Communication-aware distributed inference. We believe that the above research directions need a network-centric approach to enable the edge intelligence and, therefore, fully exploit the true potential of IoT.