# Network-Aware Optimization of Distributed Learning for Fog Computing

arXiv: https://arxiv.org/abs/2004.08488 · HTML: https://mlsanity.com/paper/2004.08488/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 27 of 27)

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling](https://mlsanity.com/paper/2112.11256/) (2021)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation](https://mlsanity.com/paper/2101.00787/) (2021)
- [On the Utility of Gradient Compression in Distributed Training Systems](https://mlsanity.com/paper/2103.00543/) (2021)
- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [SplitGP: Achieving Both Generalization and Personalization in Federated Learning](https://mlsanity.com/paper/2212.08343/) (2022)
- [Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks](https://mlsanity.com/paper/2202.02947/) (2022)
- [Federated Learning for Data Streams](https://mlsanity.com/paper/2301.01542/) (2023)
- [A Bayesian Optimization Framework for Finding Local Optima in Expensive Multi-Modal Functions](https://mlsanity.com/paper/2210.06635/) (2022)
- [Delay-Aware Hierarchical Federated Learning](https://mlsanity.com/paper/2303.12414/) (2023)
- [Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2404.13804/) (2024)
- [FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems](https://mlsanity.com/paper/2107.02755/) (2021)
- [Multi-Source to Multi-Target Decentralized Federated Domain Adaptation](https://mlsanity.com/paper/2304.12422/) (2023)
- [Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing](https://mlsanity.com/paper/2203.12738/) (2022)
- [Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks](https://mlsanity.com/paper/2311.04350/) (2023)
- [Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost](https://mlsanity.com/paper/2201.07402/) (2022)
- [Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioning](https://mlsanity.com/paper/2310.17890/) (2023)
- [Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets](https://mlsanity.com/paper/2408.09762/) (2024)
- [Fast AI Model Partition for Split Learning over Edge Networks](https://mlsanity.com/paper/2507.01041/) (2025)
- [Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling](https://mlsanity.com/paper/2109.03350/) (2021)
- [Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN](https://mlsanity.com/paper/2208.04229/) (2022)
- [Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering](https://mlsanity.com/paper/2211.12640/) (2022)
- [Coded Matrix Computations for D2D-enabled Linearized Federated Learning](https://mlsanity.com/paper/2302.12305/) (2023)
- [GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing](https://mlsanity.com/paper/2305.14562/) (2023)
- [Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks](https://mlsanity.com/paper/2605.09813/) (2026)
- [A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions](https://mlsanity.com/paper/2605.11010/) (2026)

## Abstract (arXiv)

Fog computing promises to enable machine learning tasks to scale to large amounts of data by distributing processing across connected devices. Two key challenges to achieving this goal are heterogeneity in devices compute resources and topology constraints on which devices can communicate with each other. We address these challenges by developing the first network-aware distributed learning optimization methodology where devices optimally share local data processing and send their learnt parameters to a server for aggregation at certain time intervals. Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks to each other, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points. We analytically characterize the optimal data transfer solution for different fog network topologies, showing for example that the value of offloading is approximately linear in the range of computing costs in the network. Our subsequent experiments on testbed datasets we collect confirm that our algorithms are able to improve network resource utilization substantially without sacrificing the accuracy of the learned model. In these experiments, we also study the effect of network dynamics, quantifying the impact of nodes entering or exiting the network on model learning and resource costs.