Exploring FL and secure multi-party computation
FL is an ML approach that enables the training of models across multiple devices or servers without centrally aggregating the raw data. In traditional ML, data is usually collected and sent to a central compute server for training, which raises privacy and security concerns, especially when dealing with sensitive or personal information.
In FL, the training process happens locally on the devices or nodes (for example smartphones, edge devices, or compute instances) that generate or store the data. These nodes collaborate by sharing only model updates (gradients) rather than the raw data itself. The central compute server aggregates these updates to create an improved global model. This process is repeated iteratively, with each node contributing to the model’s improvement while keeping its data private.
The main advantages of FL are as follows:
- Privacy: As the raw data remains on the local nodes, there is no need...
