Coded Computing(編碼計算)到底是什麼？
Coded Computing 編碼計算 - We consider the problem of coded computing, where a computational task is performed in a distributed fashion in the presence of adversarial workers. [1] Coded computing is a technique that enables straggler-resistant computation. [2] Coded computing is an effective technique to mitigate “stragglers” in large-scale and distributed matrix multiplication. [3] Recent results have shown that coded computing can be used to reduce the negative effect of elasticity and stragglers. [4] We study the numerical stability of polynomial based encoding methods, which has emerged to be a powerful class of techniques for providing straggler and fault tolerance in the area of coded computing. [5] Coded computing is a new framework to address fundamental issues in large scale distributed computing, by injecting structured randomness and redundancy. [6] We consider the problem of coded computing, where a computational task is performed in a distributed fashion in the presence of adversarial workers. [7] The problem of data exchange between multiple nodes with (not necessarily uniform) storage and communication capabilities models several current multi-user communication problems like Coded Caching, Data shuffling, Coded Computing, etc. [8] The problem of data exchange between multiple nodes with storage and communication capabilities models several current multi-user communication problems like Coded Caching, Data Shuffling, Coded Computing, etc. [9] This article addresses these problems by leveraging recent advances in coded computing and the deep dueling neural network architecture. [10] In this paper, we focus on practical computing systems with heterogeneous computing resources, and design a novel CDC approach, called batch-processing based coded computing (BPCC), which exploits the fact that every computing node can obtain some coded results before it completes the whole task. [11] In this work, we apply "coded computing" to protein folding simulations in an error-prone environment. [12] Experimental results show that our methods have improved the accuracy of stochastic computation and preserved the stochastic computing correlation without the need for conversion from SC to the conventional binary-encoded computing, and vice versa. [13] We then identify machine learning and coded computing as key enabling technologies to address and exploit mobility in VeFNs. [14]我們考慮編碼計算的問題，其中計算任務在對抗性工作者存在的情況下以分佈式方式執行。 [1] 編碼計算是一種能夠實現抗落後計算的技術。 [2] 編碼計算是減少大規模分佈式矩陣乘法中“落後者”的有效技術。 [3] 最近的結果表明，編碼計算可用於減少彈性和落後者的負面影響。 [4] 我們研究了基於多項式的編碼方法的數值穩定性，它已成為在編碼計算領域提供散亂和容錯的一類強大技術。 [5] 編碼計算是一種新框架，通過注入結構化隨機性和冗餘來解決大規模分佈式計算中的基本問題。 [6] 我們考慮編碼計算的問題，其中計算任務在對抗性工作者存在的情況下以分佈式方式執行。 [7] 具有（不一定是統一的）存儲和通信能力的多個節點之間的數據交換問題模擬了當前的幾個多用戶通信問題，如編碼緩存、數據混洗、編碼計算等。 [8] 具有存儲和通信能力的多個節點之間的數據交換問題模擬了當前的幾種多用戶通信問題，如編碼緩存、數據混洗、編碼計算等。 [9] 本文通過利用編碼計算和深度決斗神經網絡架構的最新進展來解決這些問題。 [10] 在本文中，我們專注於具有異構計算資源的實際計算系統，並設計了一種新穎的CDC方法，稱為基於批處理的編碼計算（BPCC），它利用了每個計算節點在完成之前可以獲得一些編碼結果的事實。整個任務。 [11] 在這項工作中，我們將“編碼計算”應用於容易出錯的環境中的蛋白質折疊模擬。 [12] 實驗結果表明，我們的方法提高了隨機計算的準確性，並保留了隨機計算的相關性，而不需要從 SC 轉換為傳統的二進制編碼計算，反之亦然。 [13] 然後，我們將機器學習和編碼計算確定為解決和利用 VeFN 移動性的關鍵支持技術。 [14]
Lagrange Coded Computing 拉格朗日編碼計算
, the maximum number of adversarial workers can be tolerated such that the correct results can be obtained) provided by the recent proposed Lagrange Coded Computing (LCC) can be extremely low if the degree of the polynomial is high. [1] Current state-of-art approaches are based on either exclusively matrix-partitioning (Entangled Polynomial (EP) Codes for matrix multiplication), or exclusively batch processing (Lagrange Coded Computing (LCC) for，如果多項式的次數很高，則最近提出的拉格朗日編碼計算（LCC）提供的對抗性工作者的最大數量可以被容忍，從而可以獲得正確的結果）可能會非常低。 [1] 當前最先進的方法要么僅基於矩陣分區（用於矩陣乘法的糾纏多項式 (EP) 代碼），要么僅基於批處理（用於 <inline-formula> <tex-math notation= 的拉格朗日編碼計算 (LCC) "LaTeX">$N$ </tex-math></inline-formula>-線性計算或多元多項式計算）。 [2] 我們設計了一種基於拉格朗日編碼計算 (LCC) 的新型編碼模型，用於私有、安全和有彈性的分佈式移動邊緣計算 (MEC) 系統，其中多個基站 (BS) 充當主機，將其計算卸載到充當工作者的邊緣節點。 [3] 我們為基於拉格朗日編碼計算 (LCC) 的物聯網 (IoT) 系統開發了一種新的拉格朗日編碼區塊鏈模型。 [4] Yu 等人提出的拉格朗日編碼計算 (LCC)。 [5] 本文提出了一種基於拉格朗日編碼計算 (LCC) 的新型框架，用於在移動邊緣計算 (MEC) 網絡中快速、安全地卸載計算任務。 [6] 在本文中，我們提出了一種分佈式編碼方案，與標準的拉格朗日編碼計算方案相比，每個計算節點的計算成本更低。 [7] 拉格朗日編碼計算 (LCC) 是最近提出的一種技術，用於在分佈式環境中對任意多項式進行彈性、安全和私有計算。 [8] 諧波編碼通過使用諧波級數注入編碼冗餘，嚴格改進了基於先前工作開發的計算方案，例如 Shamir 的秘密共享和拉格朗日編碼計算。 [9]
Secure Coded Computing 安全編碼計算
We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. [1] Using the task allocation, we then design secure coded computing schemes, for two cases, (1) with redundant computation and (2) without redundant computation. [2] We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. [3] We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. [4]我們表明，當資源是異構的時，PRAC 優於已知的安全編碼計算方法。 [1] 使用任務分配，我們針對兩種情況設計安全編碼計算方案，（1）有冗餘計算和（2）沒有冗餘計算。 [2] 我們表明，當資源是異構的時，PRAC 優於已知的安全編碼計算方法。 [3] 我們表明，當資源是異構的時，PRAC 優於已知的安全編碼計算方法。 [4]
Novel Coded Computing
We propose a novel coded computing framework, CodedFedL, that injects structured coding redundancy into federated learning for mitigating stragglers and speeding up the training procedure. [1] This paper develops a novel coded computing technique for federated learning to mitigate the impact of stragglers. [2]我們提出了一種新穎的編碼計算框架 CodedFedL，它將結構化編碼冗餘注入到聯邦學習中，以減少落後者並加快訓練過程。 [1] 本文開發了一種用於聯邦學習的新型編碼計算技術，以減輕落後者的影響。 [2]
coded computing scheme 編碼計算方案
Using the task allocation, we then design secure coded computing schemes, for two cases, (1) with redundant computation and (2) without redundant computation. [1] Extensive numerical analysis of the fundamental results as well as RM- and polar-coded computing schemes demonstrate the excellence of the RM-coded computation in achieving close-to-optimal performance while having a low-complexity decoding and explicit construction. [2] Experimental results carried out on Amazon EC2 cluster show a significant reduction in the average completion time over existing coded and uncoded computing schemes. [3] In this paper, we propose a distributed coding scheme that allows for lower computation cost per computing node than the standard Lagrange Coded Computing scheme. [4] Specifically, in this work, we propose a new information-theoretical converse and a new matching coded computing scheme, that we call coded computing for straggling systems (CCS). [5] Coded distributed computing can mitigate straggling workers by introducing redundant computations; however, existing coded computing schemes are mainly designed against persistent stragglers, and partial computations at straggling workers are discarded, leading to wasted computational capacity. [6] Numerical results show a significant reduction in the average computation time over the existing coded and uncoded computing schemes. [7]使用任務分配，我們針對兩種情況設計安全編碼計算方案，（1）有冗餘計算和（2）沒有冗餘計算。 [1] 對基本結果以及 RM 和極坐標編碼計算方案的廣泛數值分析證明了 RM 編碼計算在實現接近最佳性能的同時具有低複雜度解碼和顯式構造的卓越性。 [2] 在 Amazon EC2 集群上進行的實驗結果表明，與現有的編碼和非編碼計算方案相比，平均完成時間顯著減少。 [3] 在本文中，我們提出了一種分佈式編碼方案，與標準的拉格朗日編碼計算方案相比，每個計算節點的計算成本更低。 [4] 具體來說，在這項工作中，我們提出了一種新的信息論逆向和一種新的匹配編碼計算方案，我們稱之為散亂系統編碼計算（CCS）。 [5] 編碼分佈式計算可以通過引入冗餘計算來緩解散亂的工人；然而，現有的編碼計算方案主要是針對持久的落後者設計的，並且在落後者的部分計算被丟棄，導致計算能力的浪費。 [6] 數值結果表明，與現有的編碼和未編碼計算方案相比，平均計算時間顯著減少。 [7]
coded computing framework 編碼計算框架
By leveraging the coded computing framework in order to tackle failures or stragglers in computation, we formulate this problem using contextual-combinatorial multi-armed bandits (CC-MAB), and aim to maximize the cumulative expected reward. [1] We propose a novel coded computing framework, CodedFedL, that injects structured coding redundancy into federated learning for mitigating stragglers and speeding up the training procedure. [2] We then consider a Coded Computing framework, in which the data is possibly encoded and stored at the worker nodes in order to provide robustness against nodes that may be in a bad state. [3] We then consider a Coded Computing framework, in which the data is possibly encoded and stored at the worker nodes in order to provide robustness against nodes that may be in a bad state. [4] , overhead) that is built into the coded computing frameworks by efficiently assigning work for all fast and slow nodes according to their speeds and without needing to re-distribute data. [5]通過利用編碼計算框架來解決計算中的失敗或落後者，我們使用上下文組合多臂老虎機（CC-MAB）來製定這個問題，並旨在最大化累積預期獎勵。 [1] 我們提出了一種新穎的編碼計算框架 CodedFedL，它將結構化編碼冗餘注入到聯邦學習中，以減少落後者並加快訓練過程。 [2] 然後我們考慮一個編碼計算框架，其中數據可能被編碼並存儲在工作節點上，以便為可能處於不良狀態的節點提供魯棒性。 [3] 然後我們考慮一個編碼計算框架，其中數據可能被編碼並存儲在工作節點上，以便為可能處於不良狀態的節點提供魯棒性。 [4] ，開銷）通過根據速度有效地為所有快速和慢速節點分配工作而無需重新分配數據，從而內置到編碼計算框架中。 [5]
coded computing method
We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. [1] We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. [2] We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. [3]我們表明，當資源是異構的時，PRAC 優於已知的安全編碼計算方法。 [1] 我們表明，當資源是異構的時，PRAC 優於已知的安全編碼計算方法。 [2] 我們表明，當資源是異構的時，PRAC 優於已知的安全編碼計算方法。 [3]