**Unlocking the Power of Federated Learning: SCAFFOLD vs

This content originally appeared on DEV Community and was authored by Carlos Ruiz Viquez

Unlocking the Power of Federated Learning: SCAFFOLD vs. FedAvg

In the realm of edge AI and distributed machine learning, Federated Learning (FL) has emerged as a groundbreaking approach to enable collaborative model training across diverse devices and networks. Two prominent methods, FedAvg and SCAFFOLD, have garnered significant attention for their potential to revolutionize the field. While FedAvg is a foundational method that averages models from local devices, SCAFFOLD stands out by considering device heterogeneity, offering more accurate and resilient results.

FedAvg: A Foundational Method

FedAvg, short for Federated Averaging, is a simple yet effective method that aggregates models from local devices to create a global model. This approach has been widely adopted due to its ease of implementation and low communication overhead. However, FedAvg has limitations in handling device heterogeneity, which can lead to reduced accuracy and increased sensitivity to outliers.
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This content originally appeared on DEV Community and was authored by Carlos Ruiz Viquez

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Carlos Ruiz Viquez | Sciencx (2025-09-22T18:10:12+00:00) **Unlocking the Power of Federated Learning: SCAFFOLD vs. Retrieved from https://www.scien.cx/2025/09/22/unlocking-the-power-of-federated-learning-scaffold-vs/

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