Implementing asynchronous aggregation strategies in hierarchical topologies presents several challenges and trade-offs:
Consistency and Convergence: One of the primary challenges is ensuring the consistency of the global model. In asynchronous FL, updates from workers may arrive at different times, leading to potential conflicts and inconsistencies in the model parameters. This can hinder convergence, as the global model may be updated with stale or outdated information. To address this, Flight could implement versioning for model parameters, allowing the system to track the freshness of updates and apply strategies to prioritize more recent updates.
Communication Overhead: Asynchronous aggregation can lead to increased communication overhead, as workers may send updates at different times, resulting in a higher frequency of communication between nodes. Flight can mitigate this by implementing a batching mechanism, where updates from workers are collected over a defined period before being sent to aggregators. This would reduce the number of communication events and improve overall efficiency.
Load Balancing: In hierarchical topologies, some aggregators may become bottlenecks if they receive too many updates simultaneously. Flight can extend its architecture to include load balancing mechanisms that distribute the workload more evenly across aggregators, potentially by dynamically assigning workers to different aggregators based on their current load.
Latency and Performance: Asynchronous strategies can introduce latency in the aggregation process, as the global model may not be updated until all relevant updates are received. Flight could implement a hybrid approach, where partial aggregations are performed as updates arrive, allowing for more frequent model updates while still maintaining a level of consistency.
Complexity of Implementation: The complexity of managing asynchronous updates increases with the number of nodes and the complexity of the topology. Flight can provide higher-level abstractions and utilities to simplify the implementation of asynchronous strategies, allowing users to focus on defining their FL processes without getting bogged down in the underlying complexities.
By addressing these challenges, Flight can enhance its support for asynchronous aggregation strategies in hierarchical topologies, improving the scalability and efficiency of federated learning processes.