A collaborative learning project where users self-organize to improve their ML models by sharing representations of their data or model.
The application currently uses MPI and GRPC (experimental) to enable communication between different nodes in the network. The goal of the framework to organize everything in a modular manner. That way a researcher or engineer can easily swap out different components of the framework to test their hypothesis or a new algorithm.
- O1: Benchmark existing configurations and algorithms.
- O2: Separate the communication layer (topology) from collaborative learning algorithms.
- O3: Implement a few more collaborative learning algorithms.
- O4: Write the GRPC module for the communication layer. Then we don't need to rely on MPI which requires ssh access to all the nodes.
- O5: Improve telemetry and logging for visualization of the network.
- O6: Fault tolerance and rogue clients simulation
- O7: Comprehensive documentation - https://github.com/squidfunk/mkdocs-material
- O8: Eliminate the need to add a BaseServer module, keep it backward compatible by instantiating the server as yet another node.
- O9: Build testing suite
- O10: Set up milestones for transition to full API like interface and then launch on
pip