The proposed variance-corrected model averaging algorithm can be extended to handle dynamic network topologies and asynchronous model updates in gossip learning by incorporating adaptive mechanisms.
Dynamic Network Topologies:
Adaptive Weight Rescaling: The algorithm can dynamically adjust the rescaling factor based on the network topology. For instance, nodes with higher connectivity could have a smaller rescaling factor to maintain variance, while nodes with lower connectivity might require a larger rescaling factor.
Neighbor Awareness: Nodes can communicate their connectivity status to each other, allowing them to adapt the rescaling process based on the local network structure. This way, nodes can adjust their variance correction strategy based on their neighbors' characteristics.
Asynchronous Model Updates:
Timestamped Averaging: Introducing timestamps to model updates can help nodes track the recency of received models. Nodes can prioritize more recent models in the averaging process to ensure that the most up-to-date information is considered.
Adaptive Averaging Frequency: Nodes can dynamically adjust the frequency of model averaging based on the rate of model updates. Nodes experiencing frequent updates may opt for more frequent averaging to maintain convergence efficiency.
By incorporating these adaptive mechanisms, the variance-corrected model averaging algorithm can effectively handle the challenges posed by dynamic network topologies and asynchronous model updates in gossip learning systems.