The paper investigates the robustness of decentralized learning to network disruptions, where a certain percentage of central nodes are removed from the network. Three different scenarios are considered:
Case 1: Disrupted nodes do not hold any local data, so the disruption only affects the network structure.
Case 2: Disrupted nodes hold local data, so the disruption affects both connectivity and data availability.
Case 3: Disrupted nodes hold a disproportionately larger share of the data compared to other nodes.
The authors use a Barabasi-Albert network model to represent the communication network between nodes and employ the Decentralized Averaging (DecAvg) algorithm for the decentralized learning process.
The key findings are:
Decentralized learning is remarkably robust to disruptions. Even when a significant portion of central nodes are removed, the remaining nodes can maintain high classification accuracy, with a loss of only 10-20% compared to the no-disruption case.
Knowledge persists despite disruption. Nodes that remain connected or become isolated after disruption can still retain a significant level of knowledge acquired before the disruption, provided they have access to a small local dataset.
Decentralized learning can tolerate large losses of data. Even when disrupted nodes have much larger local datasets than the others, the surviving nodes can compensate by jointly extracting knowledge from the data available in the network, with a limited reduction in overall accuracy.
The timing of the disruption has a significant impact, with later disruptions allowing nodes to acquire more knowledge before the disruption occurs, leading to better post-disruption performance.
The results demonstrate the remarkable robustness of decentralized learning to network disruptions and data loss, making it a promising approach for scenarios where data cannot leave local nodes due to privacy or real-time constraints.
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