The distributed Data Retrieval (DR) model consists of k peers connected by a complete peer-to-peer communication network, and a trusted external data source that stores an array X of n bits (n ≫ k). Up to β k of the peers might fail in any execution (for β ∈ [0, 1)). Peers can obtain the information either by inexpensive messages passed among themselves or through expensive queries to the source array X. In the DR model, we focus on designing protocols that minimize the number of queries performed by any nonfaulty peer (a measure referred to as the query complexity) while maximizing the resiliency parameter β. The Download problem requires each nonfaulty peer to correctly learn the entire array X. Earlier work on this problem focused on synchronous communication networks and established several deterministic and randomized upper and lower bounds. Our work is the first to extend the study of distributed data retrieval to asynchronous communication networks. We address the Download problem under both the Byzantine and crash failure models. We present query-optimal deterministic solutions in an asynchronous model that can tolerate any fixed fraction β < 1 of crash faults. In the Byzantine failure model, it is known that deterministic protocols incur a query complexity of Ω(n) per peer, even under synchrony. We extend this lower bound to randomized protocols in the asynchronous model for β ≥ 1/2, and further show that for β < 1/2, a randomized protocol exists with near-optimal query complexity.

We study fault-tolerant consensus in a variant of the synchronous message passing model, where, in each round, every node can choose to be awake or asleep. This is known as the sleeping model (Chatterjee, Gmyr, Pandurangan PODC 2020) and defines the awake complexity (also called energy complexity), which measures the maximum number of rounds that any node is awake throughout the execution. Only awake nodes can send and receive messages in a given round and all messages sent to sleeping nodes are lost. We present new deterministic consensus algorithms that tolerate up to f < n crash failures, where n is the number of nodes. Our algorithms match the optimal time complexity lower bound of f+1 rounds. For multi-value consensus, where the input values are chosen from some possibly large set, we achieve an energy complexity of 𝒪(⌈ f² / n ⌉) rounds, whereas for binary consensus, we show an algorithm to achieve 𝒪(⌈ f / √n ⌉) energy complexity.

We consider the Download problem in the Data Retrieval Model, introduced in (DISC'24), where a distributed set of peers, some of which may be Byzantine, seek to learn n bits of data stored at a trustworthy external data source. Each bit of data can be learned by a peer either through a direct (costly) query of the source or through other peers that have already learned it; the goal is to design a collaborative protocol that reduces the maximum number of bits queried by any one peer ("query complexity"). We achieve optimal query complexity in a synchronous fully connected network with resilience to any constant fraction β < 1 of Byzantine peers, under varying assumptions regarding time and message size.

We consider the Download problem in the Data Retrieval Model, introduced in DISC'24, where a distributed set of peers, some of which may be Byzantine, seek to learn n bits of data stored at a trustworthy external data source. Each bit of data can be learned by a peer either through a direct and costly query of the source or through other peers that have already learned it; the goal is to design a collaborative protocol that reduces the query complexity defined as the maximum number of bits queried by any honest peer. We begin with a randomized protocol for the Download problem that achieves optimal query complexity, up to a logarithmic factor. For a stronger "dynamic" adversary that can change the set of Byzantine peers from one round to the next, we achieve optimality (within log factors) for both query complexity (in expectation) and time complexity, but with larger messages. In broadcast communication, where all peers (including Byzantine peers) are required to send the same message to all peers, we achieve (up to log factors) an optimal trade-off between query complexity, time complexity, and message size with the dynamic adversary. All of our protocols can tolerate any constant fraction β < 1 of Byzantine peers.

The distributed Data Retrieval (DR) model consists of k peers connected by a complete peer-to-peer communication network, and a trusted external data source that stores an array X of n bits (n ≫ k). Up to β k of the peers might fail in any execution (for β ∈ [0, 1)). Peers can obtain the information either by inexpensive messages passed among themselves or through expensive queries to the source array X. In the DR model, we focus on designing protocols that minimize the number of queries performed by any nonfaulty peer (a measure referred to as query complexity) while maximizing the resilience parameter β. The Download problem requires each nonfaulty peer to correctly learn the entire array X. Earlier work on this problem focused on synchronous communication networks and established several deterministic and randomized upper and lower bounds. Our work is the first to extend the study of distributed data retrieval to asynchronous communication networks. We address the Download problem under both the Byzantine and crash failure models. We present query-optimal deterministic solutions in an asynchronous model that can tolerate any fixed fraction β < 1 of crash faults. In the Byzantine failure model, it is known that deterministic protocols incur a query complexity of Ω(n) per peer, even under synchrony. We extend this lower bound to randomized protocols in the asynchronous model for β ≥ 1/2, and further show that for β < 1/2, a randomized protocol exists with near-optimal query complexity. To the best of our knowledge, this is the first work to address the Download problem in asynchronous communication networks.

We study a framework for modeling distributed network systems assisted by a reliable and powerful cloud service. Our framework aims at capturing hybrid systems based on a point to point message passing network of machines, with the additional capability of being able to access the services of a trusted high-performance external entity (the cloud). We focus on one concrete aspect that was not studied before, namely, ways of utilizing the cloud assistance in order to attain increased resilience against Byzantine behavior of machines in the network. Our network is modeled as a congested clique comprising $k$ machines that are completely connected to form a clique and can communicate with each other by passing small messages. In every execution, up to $βk$ machines (for suitable values of $β\in [0, 1)$) are allowed to be Byzantine, i.e., behave maliciously including colluding with each other, with the remaining $γk$ or more machines being \emph{honest} (for $γ=1-β$). Additionally, the machines in our congested clique can access data through a trusted cloud via queries. This externality of the data captures many real-world distributed computing scenarios and provides a natural context for exploring Byzantine resilience for essentially all conceivable problems. Moreover, we are no longer bound by the usual limits of $β< 1/3$ or even $β< 1/2$ that are typically seen in Byzantine Agreement. We focus on a few fundamental problems. We start with the ${\textsf{Download}}$ problem, wherein the cloud stores $n$ bits and these $n$ bits must be downloaded to all of the $k$ machines. In addition to ${\textsf{Download}}$, we also consider the problem of computing the ${\textsf{Disjunction}}$ and ${\textsf{Parity}}$ of the bits in the cloud. We study these problems under several settings comprising various $β$ values and adversarial capabilities.