HSQ reduces communication costs in federated learning.
problem High cost of communicating gradients in federated learning.
method Hyper-sphere quantization (HSQ) framework for efficient gradient compression.
result HSQ achieves O(logd) per-iteration communication cost, significantly reducing costs without compromising accuracy. Federated Q-Learning achieves linear regret speedup with low communication cost.
problem Achieving linear regret speedup in federated reinforcement learning without high communication costs.
method Proposed two federated Q-Learning algorithms: FedQ-Hoeffding and FedQ-Bernstein, using event-triggered synchronization, novel step size selection, and concentration inequalities.
result Total regrets achieve linear speedup compared to single-agent counterparts with logarithmic communication cost.
New algorithm reduces individual regret and communication costs in cooperative bandits.
problem Optimal individual and group regret in cooperative multi-agent bandits.
method Integrates a new communication policy into a learning algorithm.
result Achieves optimal individual regret and constant communication costs.
New RL algorithms reduce costs for single-agent and federated learning.
problem Minimizing costs in RL and federated RL settings.
method Q-EarlySettled-LowCost and FedQ-EarlySettled-LowCost algorithms.
result First algorithms to achieve low burn-in and logarithmic switching costs.
A new parallel algorithm for learning optimal policies in MDPs with low communication costs.
problem Learning optimal policies for infinite-horizon MDPs.
method Primal-Dual Stochastic Mirror Descent for convex programming problems with inexact constraints.
result First parallel algorithm for average-reward MDPs with generative model and low communication costs.
Paper proposes FedQ-Advantage for federated Q-learning with near-optimal regret and low communication cost.
problem Near-optimal federated Q-learning with low communication cost.
method Reference-advantage decomposition for variance reduction, synchronization between agents and server, policy update.
result Achieves almost optimal regret and near-linear regret speedup compared to single-agent learning.
SSTQ improves privacy-preserving vector quantization with low communication cost.
problem Achieving local differential privacy in distributed optimization with low communication cost.
method Combines overcomplete equal-norm tight frames, coordinate subsampling, and privacy-aware one-dimensional quantization.
result Achieves optimal mean squared error scaling with only ⌈log2N⌉+b bits per client. LC-FL uses generative models to reduce communication costs in federated learning.
problem High communication costs and strict model homogeneity in federated learning.
method LC-FL employs generative models to transmit data and aggregate models.
result LC-FL reduces communication costs and supports heterogeneous models.
Paper studies fundamental limits of communication in distributed learning.
problem Communication efficiency in model aggregation for distributed learning.
method Rate-Distortion approach to model aggregation as a vector Gaussian CEO problem.
result Derives rate region bound and sum-rate-distortion function for model aggregation.
Study characterizes PM2.5 dynamics in Bujumbura using low-cost sensors.
problem Characterize spatio-temporal variability of PM2.5 in Bujumbura.
method Used low-cost sensors to collect data from August 2022 to August 2023. Analyzed hourly, daily, and seasonal PM2.5 concentrations using RNN with LSTM.
result Hourly and annual PM2.5 concentrations in Bujumbura exceed WHO standards.
We present a Communication-efficient Surrogate Likelihood (CSL) framework for solving distributed statistical inference problems. CSL provides a communication-efficient surrogate to the global likelihood that can be used for low-dimensional estimation, high-dimensional regularized estimation and Bayesian inference. For…
One-shot algorithm for feature-distributed kernel PCA reduces communication costs.
problem Efficiently perform kernel PCA in distributed computing environments.
method Inspired by dual relationship between sample-distributed and feature-distributed scenarios, proposes a one-shot algorithm for feature-distributed kernel PCA.
result The algorithm provides high-quality results with low communication costs, especially when eigenvalues decay fast.
Improves ML efficiency for vast, rapidly growing data.
problem Low latency and cost in ML with distributed, growing data.
method Designs ML systems exploiting ML characteristics, data structures, and data distribution.
result Improves ML latency and cost by 1-2 orders of magnitude.
We develop a secure aggregation protocol for federated learning that reduces communication and computation costs.
problem Expensive communication and privacy concerns in federated learning.
method Adapting compression-based federated techniques to additive secret sharing.
result Our protocol achieves high accuracy with low communication costs and is more efficient than prior work.
Distributed-OMP recovers sparse vectors with low communication costs.
problem High-dimensional sparse linear regression with limited computation and communication.
method Distributed orthogonal matching pursuit (OMP) scheme.
result Support of the regression vector can be recovered with linear communication per machine and logarithmic in dimension.
FedLoRU improves FL efficiency by using low-rank updates.
problem Communication inefficiency and performance reduction in Federated Learning.
method Proposes FedLoRU, a low-rank update framework for FL, which reduces communication costs while maintaining performance.
result FedLoRU achieves convergence rates similar to FedAvg and is robust to heterogeneous and large numbers of clients.
Private anchors affect how information is communicated and can improve or distort transmission.
problem How private anchors influence strategic communication and information transmission.
method Analyzed a sender-receiver game with costly reports and privately observed anchors.
result Small positive reporting costs can lead to full revelation, even with zero costs.
A new optimizer combines Newton and ADMM for faster classification.
problem Slower convergence of first-order methods in distributed learning.
method Integrates GPU-accelerated Newton solver with ADMM for multiclass classification.
result Significantly reduces the time to solution and generalization error.
Improved distributed learning with reduced communication costs.
problem Efficient communication in resource-constrained environments for distributed learning.
method Proposed a cost-effective partial communication protocol.
result Communication cost is reduced to O(logT), improving significantly on full communication. LEASGD improves privacy-preserving decentralized learning with lower communication costs.
problem Achieving efficient and private decentralized learning.
method Proposes LEASGD, a Leader-Follower Elastic Averaging Stochastic Gradient Descent algorithm.
result LEASGD outperforms state-of-the-art algorithms in terms of lower loss and reduced communication costs.
A novel method for learning Bayesian network structures from decentralized data, balancing privacy and efficiency.
problem Privacy and communication costs in learning Bayesian network structures from decentralized data.
method Fed-Sparse-BNSL, combining differential privacy with greedy updates targeting only a few relevant edges per participant.
result Achieves utility close to non-private baselines while offering stronger privacy and communication efficiency.
New framework reduces private mean estimation error with optimal efficiency.
problem Locally private mean estimation of high-dimensional vectors.
method ProjUnit framework: random projections, normalization, and optimal algorithm execution in lower dimensions.
result Optimal error up to a 1+o(1)-factor with computational efficiency and low communication complexity.
This paper provides new algorithms for distributed clustering for two popular center-based objectives, k-median and k-means. These algorithms have provable guarantees and improve communication complexity over existing approaches. Following a classic approach in clustering by \cite{har2004coresets}, we reduce the proble…
AdaQuantFL reduces communication in federated learning by adaptively quantizing model updates.
problem Efficient communication of model updates in federated learning with high-dimensional models and limited bandwidth.
method AdaQuantFL uses adaptive quantization to reduce the number of bits for model updates while maintaining low error floor.
result AdaQuantFL converges in fewer communicated bits compared to fixed quantization levels, with minimal impact on accuracy.
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.
Decentralized algorithms can potentially outperform centralized ones in certain scenarios.
problem High communication cost in centralized algorithms.
method Study of a decentralized parallel stochastic gradient descent (D-PSGD) algorithm and theoretical analysis.
result Decentralized algorithms can outperform centralized ones in specific network configurations.
New method reduces regret and communication costs in federated Q-learning.
problem Worst-case regret and communication cost bounds in federated Q-learning.
method Gap-dependent analysis leveraging MDP structures.
result Achieves logT-type regret and communication cost bounds. FedNew improves federated learning efficiency and privacy.
problem Low communication efficiency and privacy issues in Newton-type methods for federated learning.
method Introduces a two-level framework using ADMM for inverse Hessian-gradient approximation and Newton's method for global model updates, reducing communication overhead.
result FedNew achieves superior communication efficiency and privacy compared to existing methods.
To solve the big topic modeling problem, we need to reduce both time and space complexities of batch latent Dirichlet allocation (LDA) algorithms. Although parallel LDA algorithms on the multi-processor architecture have low time and space complexities, their communication costs among processors often scale linearly wi…
BICompFL tackles bi-directional compression challenges in stochastic FL, reducing communication costs by an order of magnitude.
problem Communication bottleneck in federated learning, especially with stochastic updates.
method Introduces BICompFL, a bi-directional compression approach for stochastic federated learning.
result Significantly reduces communication costs (by an order of magnitude) while maintaining accuracy.
New method reduces gradient estimation costs for large datasets.
problem Optimizing large datasets efficiently.
method Stochastically Controlled Stochastic Gradient (SCSG) method.
result Communication and computation costs are independent of dataset size.
New algorithms reduce communication costs in collaborative learning.
problem Reducing communication costs in collaborative learning.
method Distributed boosting and adaptation to classification noise.
result Communication-efficient algorithms for collaborative PAC learning robust to noise.
Near-optimal regret in distributed bandit learning with efficient communication protocols.
problem Minimizing total regret in collaborative bandit learning with limited communication.
method Proposed communication protocols for distributed multi-armed and linear bandits with near-optimal regret and efficient communication costs.
result Achieved near-optimal regret with communication costs independent of time horizon and number of arms.
FedElasticNet reduces communication costs and handles client drift in FL.
problem Expensive communication costs and client drift issues in federated learning.
method Leverages elastic net regularizers to sparsify local updates and limit client drift.
result FedElasticNet effectively resolves communication cost and client drift problems.
ComEx protocol reduces communication costs in cooperative bandits.
problem Minimizing communication costs in cooperative bandits while maintaining optimal performance.
method Developed ComEx protocol to reduce communication from Θ(T) to O(logT) messages. result Achieves state-of-the-art performance with significantly reduced communication cost.
DIGIT is a low-cost tactile sensor for in-hand manipulation.
problem Difficulty in sensing contact forces limits robotic manipulation.
method DIGIT miniaturizes and improves a vision-based tactile sensor.
result DIGIT enables better control of interactions with the environment.
CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.
problem Byzantine attacks and communication inefficiency in federated learning.
method Transformed robust aggregation for zero-order optimization under client heterogeneity.
result CyBeR-0 achieves stable performance with minimal communication costs and reduced memory usage.
A distributed algorithm reduces communication cost in linear bandits to near-optimal levels.
problem Cooperative linear bandit optimization with stochastic contexts.
method DisBE-LUCB algorithm, DecBE-LUCB algorithm, sharing information through a central server or immediate neighbors.
result Communication cost of DisBE-LUCB matches information-theoretic lower bound up to logarithmic factors.
Paper detects communities from graph signals using low-rank excitation modeling.
problem Detect communities in graphs from noisy signals.
method Model signals as graph filter outputs, apply spectral method to covariance matrix.
result Community structure can be retrieved directly from graph signals.
DADAM optimizes distributed adaptive learning with low communication costs.
problem High communication costs in parallelizing adaptive gradient methods.
method Consensus-based distributed adaptive moment estimation method.
result Outperforms centralized adaptive algorithms for certain loss functions.
Optimal inference in distributed quantile regression without stringent scaling conditions.
problem Challenges in achieving optimal inference in distributed quantile regression due to the non-smooth nature of the QR loss function.
method Double-smoothing approach applied to local and global objective functions, with a trade-off between communication cost and statistical error.
result Established a finite-sample theoretical framework for distributed QR estimators, showing a trade-off between communication cost and statistical error.
A new method reduces communication costs in decentralized optimization.
problem Decentralized optimization with non-convex cost functions.
method LU-GT method with local updates.
result LU-GT achieves the same communication complexity as Federated Learning and maintains solution quality.
D2P-Fed improves privacy and communication in federated learning.
problem Achieving both differential privacy and communication efficiency in federated learning.
method Applying discrete Gaussian noise to private data transmission.
result D2P-Fed outperforms state-of-the-art by 4.7% to 13.0% in model accuracy with one-third less communication cost.
Adaptive quantization improves SGD accuracy in data-parallel settings.
problem Fixed gradient quantization schemes lead to suboptimal performance in deep learning.
method Developed adaptive quantization schemes ALQ and AMQ that update compression schemes based on gradient statistics.
result Improved validation accuracy on CIFAR-10 and ImageNet datasets by 2% and 1% respectively.
Proposes COLA, a communication-efficient algorithm for decentralized optimization.
problem Decentralized consensus optimization over a network.
method Linearization and communication-censoring strategy to reduce computation and communication costs.
result Proven convergence and established convergence rates for COLA.
New parallel algorithms optimize on manifolds, reducing communication costs.
problem Optimization on non-Euclidean spaces like manifolds.
method Generalized parallel inference algorithms for optimization on manifolds.
result Communication-efficient and convergent algorithms for manifold optimization.
Efficiently tests discrete distributions with limited memory and communication.
problem Testing discrete distributions with constraints on memory and communication.
method Developed efficient algorithms for uniformity/identity and closeness testing in streaming and distributed models.
result Nearly-tight lower bounds on sample complexity and communication cost for uniformity testing.
Sparse random networks reduce communication in federated learning.
problem Large communication cost in federated learning.
method Freeze random weights, train stochastic binary mask to sparsify.
result Improves accuracy, reduces communication, speeds convergence.