arXiv subjects

Chunjiang Che

Publications and source records attributed to Chunjiang Che.

3 recordsLinked to original sources

arXiv2026

OptChain: Achieving Optimal Throughput of Permissionless Blockchains

We introduce \textit{OptChain}, a permissionless blockchain state machine replication (SMR) protocol that achieves optimal throughput. We first establish a theoretical upper bound on the throughput of any SMR protocol under a fixed error probability, and OptChain is the first protocol to approach this limit. Conceptually, OptChain is a sharding protocol that optimizes both vertical and horizontal scalability. Vertically, we introduce \textit{Shardis}, a novel permissionless verifiable information dispersal mechanism that maximizes intra-shard throughput to its physical limit, determined by the fastest node's bandwidth within each shard. Horizontally, we propose \textit{diffusion mining}, which ensures security as long as each shard includes at least one honest node, thereby allowing for the maximum number of shards. We provide a formal security and efficiency analysis, demonstrating that OptChain approaches the established upper bound while maintaining robust security. Finally, we implement a full prototype of OptChain and deploy it on AWS EC2 nodes across various regions. Experimental results indicate that OptChain outperforms state-of-the-art permissionless protocols and closely approaches the theoretical optimal throughput.

cs.CR↗

arXiv2024

Manifoldchain: Maximizing Blockchain Throughput via Bandwidth-Clustered Sharding

Bandwidth limitation is the major bottleneck that hinders scaling throughput of proof-of-work blockchains. To guarantee security, the mining rate of the blockchain is determined by the miners with the lowest bandwidth, resulting in an inefficient bandwidth utilization among fast miners. We propose Manifoldchain, an innovative blockchain sharding protocol that alleviates the impact of slow miners to maximize blockchain throughput. Manifoldchain utilizes a bandwidth-clustered shard formation mechanism that groups miners with similar bandwidths into the same shard. Consequently, this approach enables us to set an optimal mining rate for each shard based on its bandwidth, effectively reducing the waiting time caused by slow miners. Nevertheless, the adversary could corrupt miners with similar bandwidths, thereby concentrating hashing power and potentially creating an adversarial majority within a single shard. To counter this adversarial strategy, we introduce sharing mining, allowing the honest mining power of the entire network to participate in the secure ledger formation of each shard, thereby achieving the same level of security as an unsharded blockchain. Additionally, we introduce an asynchronous atomic commitment mechanism to ensure transaction atomicity across shards with various mining rates. Our theoretical analysis demonstrates that Manifoldchain scales linearly in throughput with the increase in shard numbers and inversely with network delay in each shard. We implement a full system prototype of Manifoldchain, comprehensively evaluated on both simulated and real-world testbeds. These experiments validate its vertical scalability with network bandwidth and horizontal scalability with network size, achieving a substantial improvement of 186% in throughput over baseline sharding protocols, for scenarios where bandwidths of miners range from 5Mbps to 60Mbps.

cs.CR↗

arXiv2021

A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee

Federated learning allows multiple participants to collaboratively train an efficient model without exposing data privacy. However, this distributed machine learning training method is prone to attacks from Byzantine clients, which interfere with the training of the global model by modifying the model or uploading the false gradient. In this paper, we propose a novel serverless federated learning framework Committee Mechanism based Federated Learning (CMFL), which can ensure the robustness of the algorithm with convergence guarantee. In CMFL, a committee system is set up to screen the uploaded local gradients. The committee system selects the local gradients rated by the elected members for the aggregation procedure through the selection strategy, and replaces the committee member through the election strategy. Based on the different considerations of model performance and defense, two opposite selection strategies are designed for the sake of both accuracy and robustness. Extensive experiments illustrate that CMFL achieves faster convergence and better accuracy than the typical Federated Learning, in the meanwhile obtaining better robustness than the traditional Byzantine-tolerant algorithms, in the manner of a decentralized approach. In addition, we theoretically analyze and prove the convergence of CMFL under different election and selection strategies, which coincides with the experimental results.

cs.LG↗