BUOCA optimizes crowdsourcing budgets by assigning more workers to hard tasks.
problem Crowdsourcing budget inefficiency due to uniform worker assignment.
method Optimal worker allocation based on task difficulty and features, without using worker profiles.
result Large budget savings (up to 49%) with minimal accuracy loss.
New model optimizes worker-task specialization for crowdsourcing.
problem Inferring correct labels from noisy answers across varying worker and task skills.
method Introduced a d-type specialization model to account for varying worker and task types, and proposed algorithms achieving optimal sample complexity. result Optimal label inference algorithms for crowdsourcing with unknown worker and task types.
A new algorithm reduces training time for distributed machine learning by dynamically assigning backup workers.
problem Time-consuming synchronization phase due to slow workers (stragglers).
method Dynamic allocation of backup workers to minimize waiting time.
result Achieves linear speedup in convergence performance with more workers.
Paper improves communication in distributed optimization, reducing worker-to-server data exchanges.
problem Efficiency in server-to-worker communication in distributed optimization.
method MARINA-P, a novel downlink compression method using correlated compressors; M3, combining MARINA-P with uplink compression.
result MARINA-P achieves provably superior server-to-worker communication complexity with increasing number of workers.
Elastic CoCoA improves convergence rate by dynamically adjusting worker count.
problem Achieving optimal convergence rate in distributed training.
method Dynamic adjustment of worker count based on convergence feedback.
result Accelerates training time by up to 5.96x compared to static settings.
Ringmaster ASGD improves Asynchronous SGD's efficiency under varying worker times.
problem Suboptimal performance of Asynchronous SGD under heterogeneous worker computation times.
method Ringmaster ASGD, a novel Asynchronous SGD method with optimal time complexity.
result Ringmaster ASGD achieves optimal time complexity under arbitrary worker heterogeneity.
Crowdsourcing provides a popular paradigm for data collection at scale. We study the problem of selecting subsets of workers from a given worker pool to maximize the accuracy under a budget constraint. One natural question is whether we should hire as many workers as the budget allows, or restrict on a small number of …
Serverless runtimes boost large-scale optimization efficiency.
problem Efficiently solving large-scale optimization problems.
method Master-worker setup with AWS Lambda, parallel optimization algorithm.
result Relative speedups up to 256 workers and efficiencies above 70% up to 64 workers.
Proposes a new method for parallelizing SGD that combines partial results from all workers.
problem Slow workers (stragglers) cause convergence issues in synchronous SGD.
method Fixes worker computation time and combines partial results from all workers.
result Improves convergence significantly compared to existing methods.
Consider unsupervised clustering of objects drawn from a discrete set, through the use of human intelligence available in crowdsourcing platforms. This paper defines and studies the problem of universal clustering using responses of crowd workers, without knowledge of worker reliability or task difficulty. We model sto…
M3RL trains a manager to infer worker minds and assign tasks for optimal collaboration.
problem Optimal coordination among self-interested agents with diverse preferences and skills.
method Mind-aware Multi-agent Management Reinforcement Learning (M^3RL) that infers worker minds and assigns tasks.
result Effective in modeling worker minds and achieving optimal ad-hoc teaming.
In crowd labeling, a large amount of unlabeled data instances are outsourced to a crowd of workers. Workers will be paid for each label they provide, but the labeling requester usually has only a limited amount of the budget. Since data instances have different levels of labeling difficulty and workers have different r…
COMRADE is a communication-efficient, Byzantine-resilient second-order optimization algorithm.
problem Byzantine failures in distributed optimization.
method COMRADE is a communication-efficient, second-order optimization algorithm that uses a simple norm-based thresholding rule to filter out Byzantine workers.
result COMRADE achieves linear-quadratic convergence and is robust against Byzantine workers.
Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous "information piece-workers", have emerged as an effective paradigm for human-powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofread…
Zeno++ improves robustness of asynchronous SGD in fully asynchronous settings.
problem Byzantine failures in fully asynchronous SGD.
method Estimates descent of loss after applying candidate gradient.
result Proves convergence for non-convex problems under Byzantine failures.
Improves distributed SGD convergence speed with reduced computation load.
problem Mitigating stragglers in distributed SGD to speed up convergence.
method Modeling communication and computation times, adapting number of workers and computation load dynamically.
result Significantly reduces computation load while improving convergence speed.
Freya PAGE optimizes nonconvex optimization with heterogeneous, asynchronous workers.
problem Optimizing nonconvex finite-sum problems with varying worker processing times.
method Freya PAGE, a parallel method robust to stragglers and adaptive to slow computations.
result Freya PAGE offers improved time complexity guarantees compared to previous methods.
A new algorithm for deep learning training reduces communication costs and improves convergence.
problem Training deep learning models efficiently with limited communication.
method Leader Stochastic Gradient Descent (LSGD) combines local and global leaders' gradients to update workers.
result LSGD outperforms existing methods in distributed training of convolutional neural networks.
New method reduces unnecessary data communication in distributed optimization.
problem Inefficient data communication in distributed optimization methods.
method Independent sparsification of local gradient estimates before communication.
result Communication of 99% of data is redundant and unnecessary.
In many machine learning applications, crowdsourcing has become the primary means for label collection. In this paper, we study the optimal error rate for aggregating labels provided by a set of non-expert workers. Under the classic Dawid-Skene model, we establish matching upper and lower bounds with an exact exponent …
We consider unreliable distributed learning systems wherein the training data is kept confidential by external workers, and the learner has to interact closely with those workers to train a model. In particular, we assume that there exists a system adversary that can adaptively compromise some workers; the compromised …
ATA optimizes task allocation in distributed machine learning.
problem Greedy task allocation leads to inefficiencies in distributed machine learning.
method Adaptive Task Allocation (ATA) adapts to unknown computation time distributions.
result ATA identifies optimal task allocation without prior knowledge of computation times.
LocalNewton reduces communication in distributed learning.
problem Communication bottleneck in distributed optimization.
method LocalNewton is a distributed second-order algorithm with local averaging, updating models locally and communicating once every few iterations.
result LocalNewton reduces communication rounds and end-to-end running time compared to state-of-the-art algorithms.
Low complexity decentralized neural net with centralized performance.
problem Training large neural networks in distributed nodes without data sharing.
method Layer-wise learning using ADMM for low complexity and centralized performance.
result Equivalent learning performance to centralized training in distributed nodes.
We consider the problem of accurately estimating the reliability of workers based on noisy labels they provide, which is a fundamental question in crowdsourcing. We propose a novel lower bound on the minimax estimation error which applies to any estimation procedure. We further propose Triangular Estimation (TE), an al…
Paper tackles Byzantine attacks in distributed learning with a new ADMM method.
problem Byzantine workers sending arbitrary messages bias distributed learning.
method Byzantine-robust stochastic ADMM exploiting separable problem structure.
result Proposed method converges to optimal solution at O(1/k) rate.
Mini-batch optimization has proven to be a powerful paradigm for large-scale learning. However, the state of the art parallel mini-batch algorithms assume synchronous operation or cyclic update orders. When worker nodes are heterogeneous (due to different computational capabilities or different communication delays), s…
A method to robustly federate learning with non-i.i.d. data and Byzantine workers.
problem Byzantine workers sending malicious messages in federated learning with non-i.i.d. data.
method Resampling strategy to reduce inner and outer variation, stochastic average gradient, robust geometric median aggregation.
result The method reaches a neighborhood of the optimal solution at a linear convergence rate and learning error depends on the number of Byzantine workers.
Study improves communication efficiency in RIS-assisted downlink communication.
problem Improving performance of RIS-aided downlink communication over heterogeneous designs.
method Distributed learning with distributionally robust optimization.
result Our algorithm achieves 50% fewer communication rounds for similar worst-case performance.
We consider the problem faced by a service platform that needs to match limited supply with demand but also to learn the attributes of new users in order to match them better in the future. We introduce a benchmark model with heterogeneous "workers" (demand) and a limited supply of "jobs" that arrive over time. Job typ…
Partial model averaging improves Federated Learning performance.
problem Periodic model averaging causes significant model discrepancy in Federated Learning.
method Proposes a partial model averaging framework that encourages local models to stay close to each other.
result Partial averaging achieves up to 2.2% higher validation accuracy than full averaging.
Crowdsourcing platforms emerged as popular venues for purchasing human intelligence at low cost for large volume of tasks. As many low-paid workers are prone to give noisy answers, a common practice is to add redundancy by assigning multiple workers to each task and then simply average out these answers. However, to fu…
This paper revisits optimal investment strategies for defined contribution pension schemes using forward preferences.
problem Optimal investment strategies derived from backward models are not time-consistent and sub-optimal in real scenarios.
method Introduces forward preferences and solves optimal investment strategies for defined contribution pension schemes.
result Constructs optimal investment strategies for defined contribution pension schemes using forward preferences.
Crowdsourced labeling recovers task types with minimal queries.
problem Labeling tasks accurately with minimal queries.
method Worker clustering, skill estimation, weighted majority voting.
result Achieves any targeted recovery accuracy with minimum queries.
Byrd-SAGA reduces variance to robustify SGD against Byzantine attacks.
problem Learning over networks with malicious Byzantine attacks.
method Byrd-SAGA uses geometric median for robust aggregation of corrected stochastic gradients.
result Byrd-SAGA achieves provably linear convergence to optimal solution in the presence of Byzantine workers.
Ringmaster LMO accelerates training in distributed systems by asynchronously updating neural networks.
problem Asynchronous training in distributed systems where workers compute gradients at different speeds.
method Introduces an asynchronous LMO-based momentum method for unconstrained stochastic nonconvex optimization.
result Establishes convergence guarantees and time complexity bounds for asynchronous LMO-based updates.
Rescaled ASGD optimizes distributed learning under heterogeneous data.
problem Vanilla ASGD biases towards a frequency-weighted average of local objectives.
method Rescale worker stepsizes by their computation times.
result Rescaled ASGD converges to the correct global objective in fixed-computation model.
Adaptive distributed SGD reduces delay in slow workers.
problem Minimizing delay in distributed SGD with stragglers.
method Adaptive policy for varying k to optimize error-runtime trade-off. result Numerical simulations confirm the effectiveness of the adaptive approach.
Enhanced nearest neighbor improves accuracy in crowdsourced data.
problem Noise in crowdsourced labels degrades classification accuracy.
method Developed two algorithms to estimate worker quality and an enhanced nearest neighbor classifier.
result Proposed methods achieve the same regret as oracle version based on expert data.
Distributed asynchronous SGD has become widely used for deep learning in large-scale systems, but remains notorious for its instability when increasing the number of workers. In this work, we study the dynamics of distributed asynchronous SGD under the lens of Lagrangian mechanics. Using this description, we introduce …
Crowdsourcing with prospect theory handles spammers in binary classification.
problem Binary classification with spammers and reject option.
method Prospect theory for worker behavior, weighted majority voting for decision fusion.
result Asymptotic system performance and correct classification probability derived.
A parallel optimization method for convex functions using Hessian sketching and debiasing.
problem Massively parallel optimization of convex functions with limited communication.
method Newton method with Hessian sketching and debiasing by workers, server averages descent directions.
result Approximation of Newton step with low-complexity adaptive sketching scheme.
Efficient decentralized learning framework reduces communication costs.
problem Efficiently solve optimization problems in distributed learning networks.
method Censored and Quantized Generalized GADMM (CQ-GGADMM) framework.
result Achieves linear convergence rate under strong convexity assumptions.
Gradient codes adapt to varying straggler counts in distributed learning.
problem Mitigating slow worker nodes (stragglers) in distributed machine learning.
method Proposes a flexible gradient coding scheme that concatenates codes for different straggler tolerances, adapting to actual straggler counts.
result Significantly lower latency compared to fixed-tolerance gradient codes.
A distributed SGD method for heterogeneous networks with hubs and workers.
problem Learning in heterogeneous multi-level networks with worker heterogeneity and varying communication.
method Multi-Level Local SGD: distributed SGD with hub-and-spoke paradigm and hub averaging.
result The method converges with error dependent on worker heterogeneity, hub network topology, and iterations.
GADMM reduces communication costs in distributed machine learning.
problem Efficiently solving distributed machine learning problems with reduced communication costs.
method Group Alternating Direction Method of Multipliers (GADMM) framework.
result GADMM converges to the optimal solution for convex loss functions and is faster and more communication-efficient than state-of-the-art algorithms.
This work addresses the instability in asynchronous data parallel optimization. It does so by introducing a novel distributed optimizer which is able to efficiently optimize a centralized model under communication constraints. The optimizer achieves this by pushing a normalized sequence of first-order gradients to a pa…
Improves parallel deep model performance by restructuring and pruning.
problem Latency in parallel deep model execution due to interdependency among sub-models.
method Layer-wise model restructuring and pruning, using ℓ0 optimization and Munkres assignment algorithm. result Significantly improves efficiency of distributed inference in terms of communication and computational complexity.