Boosting Byzantine Protocols in Large Sparse Networks with High System Assumption Coverage

ACE: Abstract Consensus Encapsulation for Liveness Boosting of State Machine Replication

With the emergence of cross-organization attack-prone byzantine fault-tolerant (BFT) systems, so-called Blockchains, providing asynchronous state machine replication (SMR) solutions is no longer a theoretical concern. This paper introduces ACE: a general framework for the software design of fault-tolerant SMR systems. We first propose a new leader-based-view (LBV) abstraction that encapsulates the core properties provided by each view in a partially synchronous consensus algorithm, designed according to the leader-based view-by-view paradigm (e.g., PBFT and Paxos). Then, we compose several LBV…

Reaching Efficient Byzantine Agreements in Bipartite Networks

For reaching efficient deterministic synchronous Byzantine agreement upon partially connected networks, the traditional broadcast primitive is extended and integrated with a general framework. With this, the Byzantine agreement is extended to fully connected bipartite networks and some bipartite bounded-degree networks. The complexity of the Byzantine agreement is lowered and optimized with the so-called Byzantine-levers under a general system structure. Some bipartite simulation of the butterfly networks and some finer properties of bipartite bounded-degree networks are also provided for buil…

压缩联邦学习中脉冲神经网络针对非全知拜占庭攻击的鲁棒性

The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks

本文研究 FL-SNN 的带宽与拜占庭安全问题，发现结合 Top-\k{appa} 稀疏化可降低通信开销并增强对非全知攻击的鲁棒性，MinMax 攻击下准确率提升约40%。

打破$O(\sqrt n)$比特壁垒：每方仅需Polylog比特的Byzantine Agreement

Breaking the $O(\sqrt n)$-Bit Barrier: Byzantine Agreement with Polylog Bits Per Party

研究Byzantine Agreement中通信不对称性是否必然，提出SRDS原语构建首个$\tilde O(1)$平衡通信协议，打破$O(\sqrt n)$壁垒并回答开放问题。

Boosting Cloud Data Analytics using Multi-Objective Optimization

Data analytics in the cloud has become an integral part of enterprise businesses. Big data analytics systems, however, still lack the ability to take user performance goals and budgetary constraints for a task, collectively referred to as task objectives, and automatically configure an analytic job to achieve these objectives. This paper presents a data analytics optimizer that can automatically determine a cluster configuration with a suitable number of cores as well as other system parameters that best meet the task objectives. At a core of our work is a principled multi-objective optimizati…