Federated learning is a technique that enables multiple devices or machines, called clients, to work together to train a machine learning model without sharing their individual data. This approach helps protect the privacy of the clients’ data while still achieving accurate model training. However, communicating the model updates between the clients can be computationally expensive and time-consuming.
To address this challenge, Koneˇcn`y et al. (2016) proposed several strategies to improve communication efficiency in federated learning:
Heterogeneous Setting
Federated learning often involves clients with different computational capacities and available bandwidth, creating a heterogeneous setting. The authors demonstrated that using the same compression ratio for all clients can lead to slower convergence. Instead, they proposed adapting the compression ratio based on the client’s computational capacity to improve communication efficiency.
Distributed Newton Methods
The authors introduced distributed Newton methods, which use incremental Hessian eigenvector sharing to reduce communication complexity. These methods achieve faster convergence than traditional federated learning methods while using less communication.
Local Newtown Methods
Local Newtown methods involve training a local model on each client and sharing the model updates with other clients in a distributed manner. The authors proposed adaptive sketching methods, which use linear regression to approximate the gradient of the objective function and reduce communication complexity. These methods achieve faster convergence than traditional federated learning methods while using less communication.
Mini-Batch FedAvg
FedAvg is a popular federated learning method that uses a mini-batch of client data to update the model. The authors proposed a variant of FedAvg called "mixed" FedAvg, which combines mini-batch and online updates to achieve faster convergence.
Communication Complexity
The authors analyzed the communication complexity of various federated learning methods and demonstrated that distributed Newton methods have the lowest communication complexity. They also showed that using a smaller batch size can lead to faster convergence but higher communication complexity.
In summary, Koneˇcn`y et al. (2016) provided strategies for improving communication efficiency in federated learning, including adaptive sketching methods, distributed Newton methods, and mixed FedAvg. These techniques can help reduce the computational burden on clients while achieving accurate model training. By understanding these strategies, researchers and practitioners can develop more efficient federated learning algorithms that balance accuracy and communication efficiency.
