In federated learning, reducing communication overhead is crucial because devices share model updates rather than raw data, leading to significant bandwidth usage. Several techniques are employed to minimize this overhead, enhancing efficiency and making the process more feasible for networks with limited bandwidth. These techniques involve compression algorithms, sparse updates, and adaptive communication strategies.
One common approach is to use model compression techniques, such as quantization and weight pruning. Quantization reduces the precision of model parameters from floating-point to lower-bit representations, which drastically reduces the size of updates sent over the network. For example, instead of sending 32-bit floating-point values, updates can be sent as 8-bit integers. Weight pruning involves removing less important weights from the model, allowing the device to send only the most significant parameters. Both of these methods significantly reduce the volume of data communicated without significantly affecting model performance.
Another effective strategy is implementing sparse updates. Instead of sending updates for all model parameters, devices can send changes only for a limited number of the most impactful parameters. This can be achieved through techniques like federated averaging with sparsity, where only updates above a certain threshold are communicated. Additionally, adaptive communication strategies aim to optimize when and how much data is communicated. For instance, devices can wait until they have accumulated a certain amount of meaningful updates before sending them, or they may adjust the frequency of communication based on network conditions. Together, these techniques work to minimize communication overhead while maintaining the integrity of the learning process.