Fair Ordering in Replicated Systems via Streaming Social Choice

• URL: http://arxiv.org/abs/2304.02730v4
• Date: Tue, 01 Oct 2024 02:58:56 GMT
• Title: Fair Ordering in Replicated Systems via Streaming Social Choice
• Authors: Geoffrey Ramseyer, Ashish Goel,
• Abstract summary: Prior work studies the question of fairly'' ordering transactions in a replicated state machine. We argue that this problem is best viewed through the lens of social choice theory.
• Score: 2.480023305418
• License:
• Abstract: Prior work studies the question of ``fairly'' ordering transactions in a replicated state machine. Each of $n$ replicas receives transactions in a possibly different order, and the system must aggregate the observed orderings into a single order. We argue that this problem is best viewed through the lens of social choice theory, in which (in the preference aggregation problem) rankings on candidates are aggregated into an election result. Two features make this problem novel. First, the number of transactions is unbounded, and an ordering must be defined over a countably infinite set. And second, decisions must be made quickly, with only partial information. Additionally, some faulty replicas might alter their reported observations; their influence on the output should be bounded and well understood. Prior work studies a ``$\gamma$-batch-order-fairness'' property, which divides an ordering into contiguous batches. If a $\gamma$ fraction of replicas receive $\tau$ before $\tau^\prime$, then $\tau^\prime$ cannot be in an earlier batch than $\tau$. We strengthen this definition to require that batches have minimal size ($\gamma$-batch-order-fairness can be vacuously satisfied by large batches) while accounting for the possibility of faulty replicas. This social choice lens enables an ordering protocol with strictly stronger fairness and liveness properties than prior work. We study the Ranked Pairs method. Analysis of how missing information moves through the algorithm allows our streaming version to know when it can output a transaction. Deliberate construction of a tiebreaking rule ensures our algorithm outputs a transaction after a bounded time (in a synchronous network). Prior work relies on a fixed choice of $\gamma$ and bound on the number of faulty replicas $f$, but our algorithm satisfies our definition for every $\frac{1}{2}<\gamma\leq 1$ simultaneously and for any $f$.

Related papers

• Online Linear Programming with Batching [18.989352151219336]
  We study Online Linear Programming (OLP) withOmega. We show that the ability to delay decisions brings better operational performance, as measured by regret. All the proposed algorithms delay decisions on customers arriving in only the first and the last batch.
  arXiv  Detail & Related papers  (2024-08-01T06:13:24Z)
• Federated Combinatorial Multi-Agent Multi-Armed Bandits [79.1700188160944]
  This paper introduces a federated learning framework tailored for online optimization with bandit. In this setting, agents subsets of arms, observe noisy rewards for these subsets without accessing individual arm information, and can cooperate and share information at specific intervals.
  arXiv  Detail & Related papers  (2024-05-09T17:40:09Z)
• Combinatorial Stochastic-Greedy Bandit [79.1700188160944]
  We propose a novelgreedy bandit (SGB) algorithm for multi-armed bandit problems when no extra information other than the joint reward of the selected set of $n$ arms at each time $tin [T]$ is observed. SGB adopts an optimized-explore-then-commit approach and is specifically designed for scenarios with a large set of base arms.
  arXiv  Detail & Related papers  (2023-12-13T11:08:25Z)
• Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks [93.00280593719513]
  We study high-dimensional multi-armed contextual bandits with batched feedback where the $T$ steps of online interactions are divided into $L$ batches. In specific, each batch collects data according to a policy that depends on previous batches and the rewards are revealed only at the end of the batch. Our algorithm achieves regret bounds comparable to those in fully sequential setting with only $mathcalO( log T)$ batches.
  arXiv  Detail & Related papers  (2023-11-22T06:06:54Z)
• Concurrent Shuffle Differential Privacy Under Continual Observation [60.127179363119936]
  We study the private continual summation problem (a.k.a. the counter problem) We show that the concurrent shuffle model allows for significantly improved error compared to a standard (single) shuffle model.
  arXiv  Detail & Related papers  (2023-01-29T20:42:54Z)
• Learning to Order for Inventory Systems with Lost Sales and Uncertain Supplies [21.690446677016247]
  We consider a lost-sales inventory control system with a lead time $L$ over a planning horizon $T$. Supply is uncertain, and is a function of the order quantity. We show that our algorithm achieves a regret (i.e. the performance gap between the cost of our algorithm and that of an optimal policy over $T$ periods) of $O(L+sqrtT)$ when $Lgeqlog(T)$.
  arXiv  Detail & Related papers  (2022-07-10T22:11:32Z)
• Exponential Separation between Quantum and Classical Ordered Binary Decision Diagrams, Reordering Method and Hierarchies [68.93512627479197]
  We study quantum Ordered Binary Decision Diagrams($OBDD$) model. We prove lower bounds and upper bounds for OBDD with arbitrary order of input variables. We extend hierarchy for read$k$-times Ordered Binary Decision Diagrams ($k$-OBDD$) of width.
  arXiv  Detail & Related papers  (2022-04-22T12:37:56Z)
• A No-Free-Lunch Theorem for MultiTask Learning [19.645741778058227]
  We consider a seemingly favorable classification scenario where all tasks $P_t$ share a common optimal classifier $h*,$. We show that, even though such regimes admit minimax rates accounting for both $n$ and $N$, no adaptive algorithm exists.
  arXiv  Detail & Related papers  (2020-06-29T03:03:29Z)
• Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations [54.42518331209581]
  We find an algorithm which finds. epsilon$-approximate stationary point (with $|nabla F(x)|le epsilon$) using. $(epsilon,gamma)$surimate random random points. Our lower bounds here are novel even in the noiseless case.
  arXiv  Detail & Related papers  (2020-06-24T04:41:43Z)
• Optimal $\delta$-Correct Best-Arm Selection for Heavy-Tailed Distributions [2.2940141855172036]
  We consider the problem of identifying the one with the maximum mean using a $delta$-correct algorithm. Lower bounds for $delta$-correct algorithms are well known. We propose a $delta$-correct algorithm that matches the lower bound as $delta$ reduces to zero.
  arXiv  Detail & Related papers  (2019-08-24T05:31:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.