## 6 Algorithms and Frameworks in Federated Learning
### Model Aggregation Methods
#### 6.3.5 Other Aggregation Methods  
Federated learning involves various model aggregation techniques that unify individual learning experiences into a global model. Basic methods like average aggregation compute the mean of the updates from client nodes, serving as a foundation for more advanced techniques. Clipped average aggregation refines this by limiting the updates within a set range to reduce distortions from outliers. Secure aggregation uses cryptographic methods like homomorphic encryption to protect client data during aggregation. Differential privacy average aggregation enhances the privacy by adding noise to the updates, balancing confidentiality with accuracy. Momentum aggregation includes past model shifts to accelerate convergence, while Bayesian aggregation applies Bayesian inference to address model parameter uncertainties. Adversarial aggregation detects and mitigates malicious updates through outlier detection and anomaly recognition. Quantization aggregation optimizes communication by compressing the model updates, and hierarchical aggregation organizes aggregation in tiers to reduce the overhead by aggregating locally before higher-level synthesis.  
Model aggregation is crucial in FL in determining the effectiveness and robustness of the global model. Choosing the appropriate method depends on the federated data's characteristics, the network architecture, and the deployment needs, with new techniques continuing to be introduced as FL advances [88].  
In Table 2, we provide a comparison of the key federated learning algorithms: FedAvg vs. alternatives.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Handling Non-IID Data** & **Minimizing Communication Costs** & **Enhancing Model Robustness** & **Key Strengths** & **Limitations** \\ \hline FedAvg & Struggles with non-IID data, as local updates may diverge significantly, slowdown model convergence. & Standard approach with moderate communication costs, overhead. & Basic model robustness, but vulnerable to model poisoning and adversarial attacks. & Simple and widely used; baseline for FL performance. & Simple and widely non-IID data; lacks advanced security mechanisms. \\ \hline FedProx & Modifies FedAvg by adding a proximal term to handle system heterogeneity and non-IID data better. & Similar to FedAvg, but due to proximal term calculations. & Improved robustness in non-IID settings by reducing local updating divergence. & Better than FedAvg for non-IID data; compactness in extremely skewed non-IID cases. & Higher computational cases; still struggles cases. \\ \hline SCAFFOLD & Specifically designed to handle non-IID data by reducing client drift through variance reduction methods. & Slightly higher communication cost due to control variates exchanged between clients and the server. & Highly robust to non-IID data due to its focus on reducing local control drift. & Best for non-IID data handling; communication costs; complexity in implementation. \\ \hline FedOpt & Uses adaptive optimizers & Can be tuned to reduce communication & Improved model robustness due to better optimization & Adaptable optimization; but as widely tested as communication efficiency and accuracy. & Complexity in tuning; not as widely tested as communication efficiency and accuracy. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of key federated learning algorithms: FedAvg vs. alternatives.