**Fair Comparison of Two Federated Learning Approaches: FedA

Fair Comparison of Two Federated Learning Approaches: FedAvg vs. SCAFFOLD

In the realm of federated learning (FL), two prominent approaches stand out: the popular FedAvg (Local Update Aggregation) and the more recent SCAFFOLD (Controlled Serverless Adaptive Federated Dropout Learning). While both methods address the challenges of decentralized data and non-IID (non-independent and identically distributed) data, they differ significantly in their design and performance.

FedAvg: A Brief Overview

FedAvg, proposed by MC Konečny et al. in 2016, is a straightforward approach to FL. It aggregates the local updates from each participating client, using a weighted average based on the number of iterations performed by each client. FedAvg's simplicity makes it an attractive choice, but it also inherits the limitations of local update aggregation.

SCAFFOLD: A Novel Approach

SCAFFOLD, introduced by Karim et al. in 2020, builds upon the concept of serverless adaptive federated learning. It modifies the local update process by introducing a dropout mechanism and control variables to balance the trade-off between model accuracy and communication efficiency. This innovative approach aims to address the issues of non-IID data and biased gradients.

Comparison of the Two Approaches

Performance: Experiments have shown that SCAFFOLD consistently outperforms FedAvg in scenarios with non-IID data. This is due to its ability to adapt to the local data distribution and reduce the impact of biased gradients.

Communication Efficiency: SCAFFOLD reduces the communication overhead compared to FedAvg by utilizing control variables and a dropout mechanism. This results in fewer rounds of communication, making it more suitable for large-scale FL deployments.

Robustness to Adversarial Attacks: SCAFFOLD exhibits improved robustness to adversarial attacks due to its adaptive nature and ability to detect anomalies in the local updates.

Conclusion

After comparing FedAvg and SCAFFOLD, it becomes evident that SCAFFOLD offers a more robust and efficient approach to federated learning. Its innovative design and adaptability to non-IID data distributions make it a more reliable choice for applications that require high accuracy and communication efficiency. While FedAvg's simplicity and ease of implementation are attractive, its limitations and vulnerability to biased gradients make SCAFFOLD a more compelling option for large-scale FL deployments.

---

Publicado automáticamente