TDC-Cache: A Trustworthy Decentralized Cooperative Caching Framework for Web3.0
🤖 AI Summary
To address inefficiency from redundant replication and security risks arising from data inconsistency in Web3.0 decentralized data access, this paper proposes the first trusted distributed caching framework for Web3.0. Methodologically, it innovatively integrates a deep reinforcement learning–driven dynamic caching strategy (DRL-DC) with a collaborative learning–based consensus mechanism (Proof-of-Collaborative-Learning, PoCL), unifying consistency and robustness in cache decision-making; it further incorporates a decentralized oracle network (DON) and immutable storage layers (e.g., IPFS and Arweave). Experiments demonstrate a 20% reduction in average access latency, up to an 18% improvement in cache hit rate, and a 10% increase in consensus success rate—outperforming state-of-the-art approaches across key metrics. The core contribution lies in the synergistic co-design of DRL-DC and PoCL, establishing a verifiable, adaptive, and highly consistent paradigm for decentralized caching.
Application Category
📝 Abstract
The rapid growth of Web3.0 is transforming the Internet from a centralized structure to decentralized, which empowers users with unprecedented self-sovereignty over their own data. However, in the context of decentralized data access within Web3.0, it is imperative to cope with efficiency concerns caused by the replication of redundant data, as well as security vulnerabilities caused by data inconsistency. To address these challenges, we develop a Trustworthy Decentralized Cooperative Caching (TDC-Cache) framework for Web3.0 to ensure efficient caching and enhance system resilience against adversarial threats. This framework features a two-layer architecture, wherein the Decentralized Oracle Network (DON) layer serves as a trusted intermediary platform for decentralized caching, bridging the contents from decentralized storage and the content requests from users. In light of the complexity of Web3.0 network topologies and data flows, we propose a Deep Reinforcement Learning-Based Decentralized Caching (DRL-DC) for TDC-Cache to dynamically optimize caching strategies of distributed oracles. Furthermore, we develop a Proof of Cooperative Learning (PoCL) consensus to maintain the consistency of decentralized caching decisions within DON. Experimental results show that, compared with existing approaches, the proposed framework reduces average access latency by 20%, increases the cache hit rate by at most 18%, and improves the average success consensus rate by 10%. Overall, this paper serves as a first foray into the investigation of decentralized caching framework and strategy for Web3.0.
Problem
Research questions and friction points this paper is trying to address.
Addresses efficiency and security in decentralized Web3.0 data access
Proposes a caching framework to reduce latency and enhance resilience
Optimizes caching strategies using deep reinforcement learning and consensus
Innovation
Methods, ideas, or system contributions that make the work stand out.
Two-layer architecture with Decentralized Oracle Network for trusted caching
Deep Reinforcement Learning optimizes dynamic caching strategies
Proof of Cooperative Learning consensus ensures caching decision consistency
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