This paper introduces novel mechanisms to lower the communication and computational complexity of Byzantine reliable broadcast protocols in asynchronous networks with less than a third of Byzantine nodes. Two algorithms are presented that achieve an overhead factor of 2 and 3/2 respectively, while also optimizing the time complexity.
Oper, a generic transformation, enables translating any deterministic synchronous Byzantine agreement algorithm to the partially synchronous setting while preserving its optimal per-process bit complexity and latency.
Federated Computing enables collaborative data processing and machine learning across distributed devices without compromising individual data privacy.
Selective Population Protocols introduce efficient solutions for distributed computing problems.
Lumiere enables optimal BFT consensus solutions in partial synchrony with 푂(푛2) worst-case communication complexity.
Lumiere introduces an optimistically responsive BVS protocol for BFT consensus solutions in partial synchrony, maintaining optimal worst-case communication complexity.
Efficiently compute the median using selective population protocols.
Developing robust distributed methods for solving linear systems in the presence of adversaries is crucial for practical applications.
The authors present Ext, an error-free synchronous Byzantine agreement algorithm with external validity, achieving near-optimal bit complexity. They address limitations of existing algorithms by focusing on practical applications like state machine replication and blockchain protocols.
The author compares task graph scheduling algorithms using an adversarial approach to reveal performance discrepancies, highlighting the limitations of traditional benchmarking methods.