Decentralized Federated Learning: A Segmented Gossip Approach
This addresses network capacity challenges in federated learning for privacy-sensitive applications, though it appears incremental as it builds on existing decentralized concepts.
The paper tackles the inefficiency of centralized federated learning in real-world networks with limited bandwidth by proposing a decentralized, segmented gossip approach, which reduces training time compared to centralized methods.
The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized topologies and the assumption of large nodes-to-server bandwidths. However, in real-world federated learning scenarios the network capacities between nodes are highly uniformly distributed and smaller than that in a datacenter. It is of great challenges for conventional federated learning approaches to efficiently utilize network capacities between nodes. In this paper, we propose a model segment level decentralized federated learning to tackle this problem. In particular, we propose a segmented gossip approach, which not only makes full utilization of node-to-node bandwidth, but also has good training convergence. The experimental results show that even the training time can be highly reduced as compared to centralized federated learning.