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The growth of data, the need for scalability and the complexity of models used in modern machine learning calls for distributed implementations. Yet, as of today, distributed machine learning frameworks have largely ignored the possibility…

In this article, we investigate the solvability of $k$-set agreement among $n$ processes in distributed systems prone to different types of process failures. Specifically, we explore two scenarios: synchronous message-passing systems prone…

SBFT is a state of the art Byzantine fault tolerant permissioned blockchain system that addresses the challenges of scalability, decentralization and world-scale geo-replication. SBFTis optimized for decentralization and can easily handle…

In distributed learning systems, robustness issues may arise from two sources. On one hand, due to distributional shifts between training data and test data, the trained model could exhibit poor out-of-sample performance. On the other hand,…

Crash fault tolerant (CFT) consensus algorithms are commonly used in scenarios where system components are trusted -- e.g., enterprise settings and government infrastructure. However, CFT consensus can be broken by even a single corrupt…

With the advancement of blockchain technology, chained Byzantine Fault Tolerant (BFT) protocols have been increasingly adopted in practical systems, making their performance a crucial aspect of the study. In this paper, we introduce a…

Causal consistency is a widely used weak consistency model that allows high availability despite network partitions. There are plenty of research prototypes and industrial deployments of causally consistent distributed systems. However, as…

Recent developments in blockchain technology have inspired innovative new designs in resilient distributed and database systems. At their core, these blockchain applications typically use Byzantine fault-tolerant consensus protocols to…

In Federated Reinforcement Learning (FRL), agents aim to collaboratively learn a common task, while each agent is acting in its local environment without exchanging raw trajectories. Existing approaches for FRL either (a) do not provide any…

Given a set of robots with arbitrary initial location and no agreement on a global coordinate system, convergence requires that all robots asymptotically approach the exact same, but unknown beforehand, location. Robots are oblivious-- they…

State-of-the-art asynchronous Byzantine fault-tolerant (BFT) protocols, such as HoneyBadgerBFT, BEAT, and Dumbo, have shown a performance comparable to partially synchronous BFT protocols. This paper studies two practical directions in…

Asynchronous Byzantine fault-tolerant (BFT) consensus protocols, known for their robustness in unpredictable environments without relying on timing assumptions, are becoming increasingly vital for wireless applications. While these…

Referential integrity (RI) is an important correctness property of a shared, distributed object storage system. It is sometimes thought that enforcing RI requires a strong form of consistency. In this paper, we argue that causal consistency…

Existing Byzantine fault-tolerant (BFT) consensus protocols address only threshold failures, where the participating nodes fail independently of each other, each one fails equally likely, and the protocol's guarantees follow from a simple…

Inherent client drifts caused by data heterogeneity, as well as vulnerability to Byzantine attacks within the system, hinder effective model training and convergence in federated learning (FL). This paper presents two new frameworks, named…

Byzantine Fault Tolerant (BFT) consensus exhibits higher throughput in comparison to Proof of Work (PoW) in blockchains. But BFT-based protocols suffer from scalability problems with respect to the number of replicas in the network. The…

The practical Byzantine fault tolerant (PBFT) consensus protocol is one of the basic consensus protocols in the development of blockchain technology. At the same time, the PBFT consensus protocol forms a basis for some other important BFT…

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…

In this paper, we investigate the problem of distributed learning (DL) in the presence of Byzantine attacks. For this problem, various robust bounded aggregation (RBA) rules have been proposed at the central server to mitigate the impact of…

This paper considers the problem of Byzantine fault tolerance in distributed linear regression in a multi-agent system. However, the proposed algorithms are given for a more general class of distributed optimization problems, of which…