Editing Federated Learning (section)

== 5.6.2 Efficient Communication Protocols ==

Efficient communication protocols are vital for mitigating the communication bottleneck in FL, as frequent transmission of model updates between clients and the central server can overwhelm limited network resources. Strategies to enhance communication efficiency include:

* '''Update Sparsification''': This technique involves transmitting only the most significant updates or gradients, reducing the amount of data sent during each communication round. By focusing on the most impactful changes, update sparsification decreases communication overhead without substantially affecting model performance.

* '''Compression Algorithms''': Applying data compression methods to model updates before transmission can significantly reduce the data size. For example, using techniques like Huffman coding or run-length encoding can compress the updates, leading to more efficient communication.<sup>[3]</sup>

* '''Adaptive Communication Frequency''': Adjusting the frequency of communications based on the training progress or model convergence can help in conserving bandwidth. For instance, clients may perform multiple local training iterations before sending updates to the server, thereby reducing the number of communication rounds required.