Decentralized Federated Learning: A Segmented Gossip Approach (1908.07782v1)

Abstract: 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.

• The paper introduces a segmented gossip approach as a decentralized alternative that alleviates the bottlenecks of centralized federated learning.
• It segments the model into smaller data packets to optimize bandwidth usage during peer-to-peer synchronization.
• Empirical evaluations demonstrate reduced training times and robust model aggregation, highlighting the method's scalability.

Decentralized Federated Learning: A Segmented Gossip Approach

The paper "Decentralized Federated Learning: A Segmented Gossip Approach" discusses a novel method for enhancing the efficiency of federated learning (FL) by addressing issues related to bandwidth utilization and centralized network architectures. The authors introduce a decentralized approach using segmented gossip aggregation, aiming to overcome limitations inherent to traditional FL systems, which rely heavily on centralized parameter servers and face challenges in network capacity constraints.

Key Concepts and Methodology

Federated Learning typically involves a central server that receives model updates from distributed nodes. This centralization can lead to bottlenecks, especially in scenarios with many nodes or limited bandwidth. Addressing these limitations, the paper proposes a decentralized model where nodes exchange updates directly in a "segmented gossip" manner. This involves partitioning model parameters into non-overlapping segments and synchronizing these segments among nodes, thus optimizing peer-to-peer bandwidth usage.

Segmented Gossip Aggregation

The segmented gossip method works as follows:

1. Model Segmentation: The total model is split into multiple segments, allowing nodes to perform segment-level synchronization. This segmentation ensures better bandwidth utilization since smaller data packets are transmitted over the network.
2. Segmented Pulling: Nodes select others from which to pull different model segments, combining these into mixed models. This enables the network to handle many connections simultaneously, maximizing bandwidth usage.
3. Model Replica Aggregation: By setting a model replica parameter $R$, nodes aggregate multiple mixed models, improving the quality and consistency of the synchronized model while mitigating the risk of staleness in updates.

Experimental Results

Empirical evaluations demonstrate significant reductions in training time compared to centralized federated learning setups. By effectively utilizing available bandwidth through segmented gossip, the proposed approach achieves comparable accuracy to traditional methods while reducing communication costs.

Implications and Future Directions

The introduction of a decentralized federated learning framework signifies a move towards more resilient and efficient distributed learning models. This paper’s findings suggest that segment-level synchronization can lead to improved scalability and minimized network latency. The theoretical implications suggest robustness against node dropouts and dynamic worker participation, relevant to applications dependent on mobile and geographically distributed devices.

Convergences and Limitations

The segmented gossip method reduces communication overhead but requires careful consideration of the segmentation and aggregation parameters to ensure convergence efficiency. While the convergence speed and scalability in large networks are promising, further optimization could refine the methodology in different network environments, particularly in real-world applications where network conditions vary widely.

Conclusion

This paper contributes valuable insights into overcoming the challenges of centralized FL systems through decentralized architecture, leveraging the segmented gossip mechanism for enhanced bandwidth utilization and robustness. With continued exploration of this method and adaptation to various application scenarios, decentralized federated learning could significantly transform distributed model training in constrained network environments. Future studies might focus on optimizing segment selection algorithms and exploring dynamic network conditions to extend the applicability of decentralized FL systems.