🧩 Federated Learning Challenge: "Distributed Data Augmentation"
The Challenge: Unlocking Efficient Distributed Learning with Edge Devices
Imagine a network of 10 edge devices, each collecting data from distinct environments with unique characteristics. The data distributions across these devices vary significantly, presenting a challenge for traditional machine learning models. To address this, we'll be exploring the concept of Distributed Data Augmentation in Federated Learning.
The Problem: Data Heterogeneity and Limited Compute Resources
Edge devices often operate under limited compute resources and may collect data with varying formats, resolutions, or quality. Traditional centralized approaches to data augmentation may not account for the diverse data distributions, leading to biased models or decreased performance. Furthermore, the transfer of raw data from edge devices to a central server for processing raises concerns about data privacy and security.
**The Solu...
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