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We study a semi-asynchronous client-server perceptron trained via iterative parameter mixing (IPM-style averaging): clients run local perceptron updates and a server forms a global model by aggregating the updates that arrive in each…

Machine Learning · Computer Science 2026-05-19 Keval Jain , Anant Raj , Saurav Prakash , Girish Varma

An accountable distributed system provides means to detect deviations of system components from their expected behavior. It is natural to complement fault detection with a reconfiguration mechanism, so that the system could heal itself, by…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-12-15 Luciano Freitas de Souza , Petr Kuznetsov , Thibault Rieutord , Sara Tucci-Piergiovanni

Logging -- used for system events and security breaches to describe more informational yet essential aspects of software features -- is pervasive. Given the high transactionality of today's software, logging effectiveness can be reduced by…

Software Engineering · Computer Science 2022-07-20 Yiming Tang , Allan Spektor , Raffi Khatchadourian , Mehdi Bagherzadeh

Prompt-based tuning has emerged as a lightweight alternative to full fine-tuning in large vision-language models, enabling efficient adaptation via learned contextual prompts. This paradigm has recently been extended to federated learning…

Machine Learning · Computer Science 2025-09-09 Maozhen Zhang , Mengnan Zhao , Wei Wang , Bo Wang

BACKGROUND: Modern distributed systems replicate data across multiple execution sites. Business requirements and resource constraints often necessitate mixing different languages across replica sites. To facilitate the management of…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-12-02 Provakar Mondal , Eli Tilevich

A companion paper defined the notion of digital social contracts, presented a design for a social-contracts programming language, and demonstrated its potential utility via example social contracts. The envisioned setup consists of people…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-02-28 Ouri Poupko , Ehud Shapiro , Nimrod Talmon

Permissioned blockchains employ Byzantine fault-tolerant (BFT) state machine replication (SMR) to reach agreement on an ever-growing, linearly ordered log of transactions. A new paradigm, combined with decades of research in BFT SMR and…

Cryptography and Security · Computer Science 2021-03-02 Fangyu Gai , Ali Farahbakhsh , Jianyu Niu , Chen Feng , Ivan Beschastnikh , Hao Duan

Internet supercomputing is an approach to solving partitionable, computation-intensive problems by harnessing the power of a vast number of interconnected computers. For the problem of using network supercomputing to perform a large…

Distributed, Parallel, and Cluster Computing · Computer Science 2014-07-04 Seda Davtyan , Kishori M. Konwar , Alexander A. Shvartsman

We propose uBFT, the first State-Machine Replication (SMR) system to achieve microsecond-scale latency in data centers, while using only $2f{+}1$ replicas to tolerate $f$ Byzantine failures. The Byzantine Fault Tolerance (BFT) provided by…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-02-17 Marcos K. Aguilera , Naama Ben-David , Rachid Guerraoui , Antoine Murat , Athanasios Xygkis , Igor Zablotchi

Byzantine fault-tolerant (BFT) state machine replication (SMR) has been studied for over 30 years. Recently it has received more attention due to its application in permissioned blockchain systems. A sequence of research efforts focuses on…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-05-03 Ittai Abraham , Kartik Nayak , Ling Ren , Zhuolun Xiang

Client-side logic and storage are increasingly used in web and mobile applications to improve response time and availability. Current approaches tend to be ad-hoc and poorly integrated with the server-side logic. We present a principled…

Distributed, Parallel, and Cluster Computing · Computer Science 2013-10-14 Marek Zawirski , Annette Bieniusa , Valter Balegas , Sérgio Duarte , Carlos Baquero , Marc Shapiro , Nuno Preguiça

As an emerging paradigm of federated learning, asynchronous federated learning offers significant speed advantages over traditional synchronous federated learning. Unlike synchronous federated learning, which requires waiting for all…

Machine Learning · Computer Science 2025-04-10 Chaoyi Lu , Yiding Sun , Pengbo Li , Zhichuan Yang

Traditional approaches to replication require client requests to be ordered before making them durable by copying them to replicas. As a result, clients must wait for two round-trip times (RTTs) before updates complete. In this paper, we…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-10-30 Seo Jin Park , John Ousterhout

Retrieval Augmented Generation (RAG) expands the capabilities of modern large language models (LLMs), by anchoring, adapting, and personalizing their responses to the most relevant knowledge sources. It is particularly useful in chatbot…

Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses. This approach significantly expands LLM capabilities by leveraging vast,…

Cryptography and Security · Computer Science 2025-11-13 Haowei Wang , Rupeng Zhang , Junjie Wang , Mingyang Li , Yuekai Huang , Dandan Wang , Qing Wang

We consider a two-user random access system in which each user independently selects a coding scheme from a finite set for every message, without sharing these choices with the other user or with the receiver. The receiver aims to decode…

Information Theory · Computer Science 2025-12-01 Nazanin Mirhosseini , Jie Luo

Monitoring distributed systems to ensure their correctness is a challenging and expensive but essential problem. It is challenging because while execution of a distributed system creates a partial order among events, the monitor will…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-07-28 Vidhya Tekken Valapil , Sandeep Kulkarni , Eric Torng , Gabe Appleton

Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data. Traditionally, federated learning methods assume a fixed setting in which client data and learning objectives…

Image and Video Processing · Electrical Eng. & Systems 2025-11-18 Can Peng , Qianhui Men , Pramit Saha , Qianye Yang , Cheng Ouyang , J. Alison Noble

Replicated services are inherently vulnerable to failures and security breaches. In a long-running system, it is, therefore, indispensable to maintain a reconfiguration mechanism that would replace faulty replicas with correct ones. An…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-10-22 Petr Kuznetsov , Andrei Tonkikh

Retrieval-Augmented Generation RAG systems enhance large language models by grounding responses in external knowledge bases, but conventional RAG architectures operate with static corpora that cannot evolve from user interactions. We…

Artificial Intelligence · Computer Science 2025-12-30 Teja Chinthala