You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We consider the problem of making distributed computations robust to noise, in particular to worst-case (adversarial) corruptions of messages. We give a general distributed interactive coding scheme which simulates any asynchronous distributed protocol while tolerating an optimal corruption of a @math fraction of all messages while incurring a moderate blowup of @math in the communication complexity. Our result is the first fully distributed interactive coding scheme in which the topology of the communication network is not known in advance. Prior work required either a coordinating node to be connected to all other nodes in the network or assumed a synchronous network in which all nodes already know the complete topology of the network.

Cited Abstract(s):
[1]: We provide the first capacity approaching coding schemes that robustly simulate any interactive protocol over an adversarial channel that corrupts any @math fraction of the transmitted symbols. Our coding schemes achieve a communication rate of @math over any adversarial channel. This can be improved to @math for random, oblivious, and computationally bounded channels, or if parties have shared randomness unknown to the channel. Surprisingly, these rates exceed the @math interactive channel capacity bound which [Kol and Raz; STOC'13] recently proved for random errors. We conjecture @math and @math to be the optimal rates for their respective settings and therefore to capture the interactive channel capacity for random and adversarial errors. In addition to being very communication efficient, our randomized coding schemes have multiple other advantages. They are computationally efficient, extremely natural, and significantly simpler than prior (non-capacity approaching) schemes. In particular, our protocols do not employ any coding but allow the original protocol to be performed as-is, interspersed only by short exchanges of hash values. When hash values do not match, the parties backtrack. Our approach is, as we feel, by far the simplest and most natural explanation for why and how robust interactive communication in a noisy environment is possible.

[2]: Consider two parties who wish to communicate in order to execute some interactive protocol π. However, the communication channel between them is noisy: An adversary sees everything that is transmitted over the channel and can change a constant fraction of the bits arbitrarily, thus interrupting the execution of π (which was designed for an error-free channel). If π only contains a single long message, then a good error correcting code would overcome the noise with only a constant overhead in communication. However, this solution is not applicable to interactive protocols consisting of many short messages. Schulman [1992, 1993] introduced the notion of interactive coding: A simulator that, given any protocol π, is able to simulate it (i.e., produce its intended transcript) even in the presence of constant rate adversarial channel errors, and with only constant (multiplicative) communication overhead. However, the running time of Schulman's simulator, and of all simulators that followed, has been exponential (or subexponential) in the communication complexity of π (which we denote by N). In this work, we present three efficient simulators, all of which are randomized and have a certain failure probability (over the choice of coins). The first runs in time poly(N), has failure probability roughly 2-N, and is resilient to 1 32-fraction of adversarial error. The second runs in time O(N log N), has failure probability roughly 2-N, and is resilient to some constant fraction of adversarial error. The third runs in time O(N), has failure probability 1 poly(N), and is resilient to some constant fraction of adversarial error. (Computational complexity is measured in the RAM model.) The first two simulators can be made deterministic if they are a priori given a random string (which may be known to the adversary ahead of time). In particular, the simulators can be made to be nonuniform and deterministic (with equivalent performance).

[3]: Let the input to a computation problem be split between two processors connected by a communication link; and let an interactive protocol spl pi be known by which, on any input, the processors can solve the problem using no more than T transmissions of bits between them, provided the channel is noiseless in each direction. We study the following question: if in fact the channel is noisy, what is the effect upon the number of transmissions needed in order to solve the computation problem reliably? Technologically this concern is motivated by the increasing importance of communication as a resource in computing, and by the tradeoff in communications equipment between bandwidth, reliability, and expense. We treat a model with random channel noise. We describe a deterministic method for simulating noiseless-channel protocols on noisy channels, with only a constant slowdown. This is an analog for general, interactive protocols of Shannon's coding theorem, which deals only with data transmission, i.e., one-way protocols. We cannot use Shannon's block coding method because the bits exchanged in the protocol are determined only one at a time, dynamically, in the course of the interaction. Instead, we describe a simulation protocol using a new kind of code, explicit tree codes.

[4]: We consider the question of interactive communication, in which two remote parties perform a computation while their communication channel is (adversarially) noisy. We extend here the discussion into a more general and stronger class of noise, namely, we allow the channel to perform insertions and deletions of symbols. These types of errors may bring the parties "out of sync", so that there is no consensus regarding the current round of the protocol. In this more general noise model, we obtain the first interactive coding scheme that has a constant rate and tolerates noise rates of up to 1 18 - epsilon. To this end we develop a novel primitive we name edit distance tree code. The edit distance tree code is designed to replace the Hamming distance constraints in Schulman's tree codes (STOC 93), with a stronger edit distance requirement. However, the straightforward generalization of tree codes to edit distance does not seem to yield a primitive that suffices for communication in the presence of synchronization problems. Giving the "right" definition of edit distance tree codes is a main conceptual contribution of this work.

[5]: Communication is critical to distributed computing, parallel computing, or any situation in which automata interact-hence its significance as a resource in computation. In view of the likelihood of errors occurring in a lengthy interaction, it is desirable to incorporate this possibility in the model of communication. The author relates the noisy channel and the standard (noise less channel) complexities of a communication problem by establishing a 'two-way' or interactive analogue of Shanon's coding theorem: every noiseless channel protocol can be simulated by a private-coin noisy channel protocol whose time bound is proportional to the original (noiseless) time bound and inversely proportional to the capacity of the channel, while the protocol errs with vanishing probability. The method involves simulating the original protocol while implementing a hierarchical system of progress checks which ensure that errors of any magnitude in the simulation are, with high probability, rapidly eliminated. >

[6]: 

[7]: Error correction and message authentication are well studied in the literature, and various efficient solutions have been suggested and analyzed. This is however not the case for data streams in which the message is very long, possibly infinite, and not known in advance to the sender. Trivial solutions for error-correcting and authenticating data streams either suffer from a long delay at the receiver’s end or cannot perform well when the communication channel is noisy.

[8]: We study the interactive channel capacity of an e-noisy channel. The interactive channel capacity C(e) is defined as the minimal ratio between the communication complexity of a problem (over a non-noisy channel), and the communication complexity of the same problem over the binary symmetric channel with noise rate e, where the communication complexity tends to infinity. Our main result is the upper bound C(e) ≤ 1-Ω(√H(e)). This compares with Shannon's non-interactive channel capacity of 1-H(e). In particular, for a small enough e, our result gives the first separation between interactive and non-interactive channel capacity, answering an open problem by Schulman [Schulman1]. We complement this result by the lower bound C(e) ≥ 1-O(√H(e)), proved for the case where the players take alternating turns.

[9]: We consider the task of interactive communication in the presence of adversarial errors and present tight bounds on the tolerable error-rates in a number of different settings. Most significantly, we explore adaptive interactive communication where the communicating parties decide who should speak next based on the history of the interaction. In particular, this decision can depend on estimates of the amount of errors that have occurred so far. Braverman and Rao [STOC'11] show that non-adaptively one can code for any constant error rate below 1 4 but not more. They asked whether this bound could be improved using adaptivity. We answer this open question in the affirmative (with a slightly different collection of resources): Our adaptive coding scheme tolerates any error rate below 2 7 and we show that tolerating a higher error rate is impossible. We also show that in the setting of [CRYPTO'13], where parties share randomness not known to the adversary, adaptivity increases the tolerable error rate from 1 2 to 2 3. For list-decodable interactive communications, where each party outputs a constant size list of possible outcomes, the tight tolerable error rate is 1 2. Our negative results hold even if the communication and computation are unbounded, whereas for our positive results communication and computations are polynomially bounded. Most prior work considered coding schemes with linear communication bounds, while allowing unbounded computations. We argue that studying tolerable error rates in this relaxed context helps to identify a setting's intrinsic optimal error rate. We set forward a strong working hypothesis which stipulates that for any setting the maximum tolerable error rate is independent of many computational and communication complexity measures. We believe this hypothesis to be a powerful guideline for the design of simple, natural, and efficient coding schemes and for understanding the (im)possibilities of coding for interactive communications.

[10]: We study efficient, deterministic interactive coding schemes that simulate any interactive protocol both under random and adversarial errors, and can achieve a constant communication rate independent of the protocol length. For channels that flip bits independently with probability e For channels in which an adversary controls the noise pattern our coding scheme can tolerate Ω(1 log n) fraction of errors with rate approaching 1. Once more, all previously known nontrivial deterministic schemes (either efficient or not) in the adversarial setting had a rate bounded away from 1, and no nontrivial efficient deterministic coding schemes were known with any constant rate. Essential to both results is an explicit, efficiently encodable and decodable systematic tree code of length n that has relative distance Ω(1 log n) and rate approaching 1, defined over an O(log n)-bit alphabet. No nontrivial tree code (either efficient or not) was known to approach rate 1, and no nontrivial distance bound was known for any efficient constant rate tree code. The fact that our tree code is systematic, turns out to play an important role in obtaining rate 1 - O([EQUATION]) in the random error model, and approaching rate 1 in the adversarial error model.

[11]: We provide tight upper and lower bounds on the noise resilience of interactive communication over noisy channels with feedback. In this setting, we show that the maximal fraction of noise that any nonadaptive protocol can withstand is 1 3. In addition, we provide a simple and efficient nonadaptive coding scheme that succeeds as long as the fraction of noise is at most @math . Surprisingly, both bounds hold regardless of whether the parties send bits or symbols from an arbitrarily large alphabet. We also consider interactive communication over erasure channels. We provide a coding scheme that withstands the optimal tolerable erasure rate of @math [ , IEEE Trans. Info. Theory , 2015], but operates in a much simpler and more efficient way than the previous schemes. Our coding scheme works with an alphabet of size 4, in contrast to prior schemes in which the alphabet size grows as @math . Building on the above algorithm with a fixed alphabet size, we are able to devise a protocol for binary erasure channels that tolerates erasure rates of up to @math .

[12]: 

[13]:
Performing computations over noisy channels is the heart of , initiated by Schulman [5] [3] . A long line of work considers the 2-party case and obtains various coding schemes, as well as bounds on their capabilities in various settings and noise models [13] [6] [2] [9] [7] [11] [8] [1] [4] [10] . See [12] for a survey on interactive coding.