Federated learning utilizes several optimization algorithms to enable effective model training across distributed devices without sharing raw data. The primary goal of these algorithms is to update a global model by aggregating locally computed updates from participating clients. One of the most common methods used is Federated Averaging (FedAvg), which operates by averaging the model weights or updates from multiple clients after they complete their local updates. This approach assumes that clients have similar data distributions, allowing a straightforward aggregation for improved model performance.
In addition to FedAvg, other algorithms are also employed to address specific challenges in federated learning. For instance, Federated Stochastic Variance Reduced Gradient (FSVRG) helps mitigate the variance in local updates by incorporating techniques from stochastic optimization. This can enhance convergence speed and stability, particularly when clients have highly non-IID (independent and identically distributed) data. Furthermore, Federated Proximal (FedProx) introduces a penalty term during the optimization process, which constrains the updates of local models to prevent them from deviating too much from the global model. This is particularly beneficial when there are significant differences in client data distributions.
Moreover, optimization algorithms like FedDyn and Local SGD are also becoming popular in federated learning scenarios. FedDyn uses dynamic weight adjustments based on the clients' data distributions, while Local SGD allows for more frequent local updates, reducing the communication overhead with the central server. By deploying these various algorithms, federated learning can cater to diverse applications, balancing the trade-offs between personalized model performance and maintaining overall global model integrity across decentralized environments.