Related papers: Byzantine Fault Tolerance in Distributed Machine L…

The practical Byzantine fault tolerant (PBFT) consensus mechanism is one of the most basic consensus algorithms (or protocols) in blockchain technologies, thus its performance evaluation is an interesting and challenging topic due to a…

This paper considers the problem of Byzantine fault-tolerance in distributed multi-agent optimization. In this problem, each agent has a local cost function, and in the fault-free case, the goal is to design a distributed algorithm that…

This paper presents BFTBrain, a reinforcement learning (RL) based Byzantine fault-tolerant (BFT) system that provides significant operational benefits: a plug-and-play system suitable for a broad set of hardware and network configurations,…

Distributed multi-task learning provides significant advantages in multi-agent networks with heterogeneous data sources where agents aim to learn distinct but correlated models simultaneously.However, distributed algorithms for learning…

Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot research topic. However, even in the independent and…

This paper introduces Flexible BFT, a new approach for BFT consensus solution design revolving around two pillars, stronger resilience and diversity. The first pillar, stronger resilience, involves a new fault model called alive-but-corrupt…

In a practical Byzantine fault tolerance (PBFT) blockchain network, the voting nodes may always leave the network while some new nodes can also enter the network, thus the number of voting nodes is constantly changing. Such a new PBFT with…

A plethora of modern machine learning tasks require the utilization of large-scale distributed clusters as a critical component of the training pipeline. However, abnormal Byzantine behavior of the worker nodes can derail the training and…

Byzantine fault tolerance (BFT) consensus is a fundamental primitive for distributed computation. However, BFT protocols suffer from the ordering manipulation, in which an adversary can make front-running. Several protocols are proposed to…

Byzantine fault-tolerant (BFT) protocols allow a group of replicas to come to a consensus even when some of the replicas are Byzantine faulty. There exist multiple BFT protocols to securely tolerate an optimal number of faults $t$ under…

We present ezBFT, a novel leaderless, distributed consensus protocol capable of tolerating byzantine faults. ezBFT's main goal is to minimize the client-side latency in WAN deployments. It achieves this by (i) having no designated primary…

Several research projects have shown that Byzantine fault tolerance (BFT) is practical today in terms of performance. Deficiencies in other aspects might still be an obstacle to a more wide-spread deployment in real-world applications. One…

This paper presents DuoBFT, a Byzantine fault-tolerant protocol that uses trusted components to provide commit decisions in the Hybrid fault model in addition to commit decisions in the BFT model. By doing so, it enables the clients to…

Privacy and Byzantine resilience (BR) are two crucial requirements of modern-day distributed machine learning. The two concepts have been extensively studied individually but the question of how to combine them effectively remains…

In this paper, we study the problem of distributed training (DT) under Byzantine attacks with communication constraints. While prior work has developed various robust aggregation rules at the server to enhance robustness to Byzantine…

With the increasing importance of machine learning, the privacy and security of training data have become critical. Federated learning, which stores data in distributed nodes and shares only model parameters, has gained significant…

Federated Learning (FL) enables clients to collaboratively train a global model without sharing their private data. However, the presence of malicious (Byzantine) clients poses significant challenges to the robustness of FL, particularly…

Robust distributed learning algorithms aim to maintain reliable performance despite the presence of misbehaving workers. Such misbehaviors are commonly modeled as Byzantine failures, allowing arbitrarily corrupted communication, or as data…

With the rapid development of blockchain and its applications, the amount of data stored on decentralized storage networks (DSNs) has grown exponentially. DSNs bring together affordable storage resources from around the world to provide…

Service replication distributes an application over many processes for tolerating faults, attacks, and misbehavior among a subset of the processes. The established state-machine replication paradigm inherently requires the application to be…