Chisme: Fully Decentralized Differentiated Deep Learning for Edge Intelligence (2505.09854v1)

Abstract: As demand for intelligent services rises and edge devices become more capable, distributed learning at the network edge has emerged as a key enabling technology. While existing paradigms like federated learning (FL) and decentralized FL (DFL) enable privacy-preserving distributed learning in many scenarios, they face potential challenges in connectivity and synchronization imposed by resource-constrained and infrastructure-less environments. While more robust, gossip learning (GL) algorithms have generally been designed for homogeneous data distributions and may not suit all contexts. This paper introduces Chisme, a novel suite of protocols designed to address the challenges of implementing robust intelligence in the network edge, characterized by heterogeneous data distributions, episodic connectivity, and lack of infrastructure. Chisme includes both synchronous DFL (Chisme-DFL) and asynchronous GL (Chisme-GL) variants that enable collaborative yet decentralized model training that considers underlying data heterogeneity. We introduce a data similarity heuristic that allows agents to opportunistically infer affinity with each other using the existing communication of model updates in decentralized FL and GL. We leverage the heuristic to extend DFL's model aggregation and GL's model merge mechanisms for better personalized training while maintaining collaboration. While Chisme-DFL is a synchronous decentralized approach whose resource utilization scales linearly with network size, Chisme-GL is fully asynchronous and has a lower, constant resource requirement independent of network size. We demonstrate that Chisme methods outperform their standard counterparts in model training over distributed and heterogeneous data in network scenarios ranging from less connected and reliable networks to fully connected and lossless networks.

Decentralized Differentiated Deep Learning for Edge Intelligence

The paper "Chisme: Fully Decentralized Differentiated Deep Learning for Edge Intelligence" introduces Chisme, a novel suite of protocols aimed at enhancing the capabilities of decentralized learning systems at the network edge. This work addresses the challenges posed by heterogeneous data distributions and varying network conditions, which are prevalent in edge environments. Chisme, encompassing two main variants—Chisme-DFL and Chisme-GL—strives to optimize model training collaboratively and efficiently without central coordination.

The research begins by recognizing the limitations of existing distributed learning paradigms like Federated Learning (FL) and Gossip Learning (GL), particularly in environments with significant resource and connectivity constraints. While these paradigms aim for privacy-preserving model training, they often fall short in dealing with the inherent heterogeneity and intermittent connectivity of edge devices. Chisme is designed to overcome these limitations by incorporating and extending the concepts of Decentralized FL (DFL) and GL, adding a significant layer of differentiation in learning processes based on data distribution similarity among agents.

The core of Chisme’s innovation lies in its use of a cosine similarity-based heuristic that guides how model updates are aggregated or merged. This heuristic allows individual agents to infer the affinity between their data and that of their peers, adjusting their collaboration accordingly. Both Chisme-DFL (synchronous) and Chisme-GL (asynchronous) utilize this heuristic, ensuring that model updates reflect local data distributions more accurately. Chisme-DFL provides a linearly scalable aggregation strategy, whereas Chisme-GL operates with a constant resource requirement, irrespective of network size.

In quantitative evaluations, Chisme outperformed traditional FL and GL methods concerning model accuracy and training efficiency, particularly in settings characterized by partial connectivity and data loss. This suggests that C20ease of heterogeneity in data distributions leads to more effective personalization and reduced overfitting, even in challenging network conditions. In setups mimicking real-world scenarios, such as those presented by the FEMNIST dataset, Chisme capitalized on its differentiation capabilities to handle the naturally varied data with notable improvement over its counterparts.

While the research substantiates its proposed methods with significant numerical results, it also opens several avenues for future exploration. Immensely practical implications lie in improving edge intelligence and democratizing AI accessibility to underrepresented domains. Future developments in decentralized learning could focus on abstractions that integrate these differentiation strategies more seamlessly and efficiently. Furthermore, addressing privacy concerns and resource constraints through disruption-tolerant protocols could enhance the utility and security of such decentralized systems in real-world applications.

In essence, the Chisme approach not only reinforces the ability of decentralized systems to perform efficiently in heterogeneous and resource-constrained environments but also paves the way for more robust and personalized intelligent services at the edge. This research underscores a pivotal move towards more adaptive and resilient AI systems suitable for diverse and dynamically evolving deployment contexts.