How can Flight's hierarchical FL model be extended to support more complex network topologies, such as directed acyclic graphs or arbitrary graphs, beyond the tree-based topologies considered in this work?
To extend Flight's hierarchical Federated Learning (FL) model to support more complex network topologies, such as directed acyclic graphs (DAGs) or arbitrary graphs, several modifications can be made to the existing architecture.
Graph Representation: The current implementation uses a tree structure, which inherently limits the relationships between nodes to a parent-child hierarchy. By adopting a more flexible graph representation, such as using NetworkX to define arbitrary directed graphs, Flight can accommodate multiple parents for nodes, allowing for more complex interactions and dependencies among workers and aggregators.
Job Scheduling and Execution: In a DAG, nodes can have multiple predecessors and successors, which necessitates a more sophisticated job scheduling mechanism. Flight can implement a topological sorting algorithm to determine the order of job execution based on dependencies. This would ensure that a node only begins processing once all its parent nodes have completed their tasks.
Dynamic Node Management: The ability to dynamically add or remove nodes from the graph during runtime can enhance the flexibility of the system. This could involve implementing a mechanism for nodes to register themselves with the Coordinator and update their status, allowing for real-time adjustments to the network topology based on device availability or performance.
Aggregation Strategies: The aggregation process would need to be adapted to handle multiple incoming updates from various nodes. This could involve developing new aggregation strategies that consider the contributions from multiple parents, potentially using weighted averages based on the reliability or performance of the contributing nodes.
Fault Tolerance: With more complex topologies, the system must be robust against node failures. Implementing redundancy and fallback mechanisms, such as reassigning tasks to alternative nodes in case of failure, would be crucial for maintaining the integrity of the FL process.
By incorporating these enhancements, Flight can effectively support more complex network topologies, enabling it to better model real-world distributed systems and improve the efficiency and scalability of federated learning processes.