FedSTaS stratifies and samples clients for efficient FL.
problem Inefficient client sampling in federated learning.
method Stratifies clients based on compressed gradients, uses Neyman allocation for sampling, and samples local data uniformly.
result FedSTaS achieves higher accuracy than FedSTS in fixed training rounds.
Optimal client sampling reduces communication in federated learning.
problem Efficiently aggregate model updates from distributed clients in federated learning.
method Model weights as an Ornstein-Uhlenbeck process to estimate uncommunicated updates; optimal client sampling strategy.
result Significant reduction in communication with competitive or superior performance.
FedAMD framework improves federated learning with partial client participation.
problem Data heterogeneity and inactive client updates in partial client participation.
method Anchor sampling divides clients into anchor and miner groups, using large and small batches respectively.
result FedAMD achieves faster convergence and improved model performance compared to state-of-the-art methods.
New method for decentralized learning from diverse clients.
problem Achieving high-performance global model from diverse client models.
method Decentralized Learning via Adaptive Distillation (DLAD).
result Demonstrates effectiveness of DLAD on multiple public datasets.
This work enables privacy-preserving model learning from single samples per client.
problem Learning from devices with only one sample each, especially in early rounds.
method Injects a single, calibrated noisy perturbation to transform data, then aggregates and processes for unbiased gradient update.
result Enables accurate, privacy-preserving model learning from devices with limited data.
New method improves FL efficiency by shuffling data, balancing privacy and accuracy.
problem Balancing privacy, communication, and accuracy in federated learning.
method Developed communication-efficient schemes for private mean estimation, combining privacy amplification and shuffled data.
result Achieved same privacy, optimization performance with lower communication cost.
Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, then averaging the gradients. Downloading models and uploading gradients uses the client's bandwidth, so minimizing these transmission costs is…
Federated learning supports exact support recovery with minimal communication.
problem Learning the exact support of sparse linear regression in federated learning.
method One-shot communication algorithm for exact support recovery without optimization.
result Polynomial sample complexity and logarithmic number of clients required.
New defense method protects client data privacy in federated learning.
problem Gradient leakage attacks in federated learning.
method Learning to obscure data to generate synthetic samples.
result Synthetic samples preserve predictive features and protect privacy.
Study shows data heterogeneity affects distributed learning's generalization error.
problem Effect of data heterogeneity on distributed learning performance.
method Established bounds on generalization error using information-theoretic rate-distortion theory.
result Data heterogeneity improves generalization error for distributed learning.
New algorithm achieves linear speedup in non-i.i.d. federated bilevel learning.
problem Linear speedup in convergence for non-i.i.d. datasets in federated bilevel optimization.
method Proposes FedMBO with a novel client sampling scheme for non-i.i.d. datasets.
result Achieves a convergence rate of O(1/√(nK) + 1/K + √n/K³/²).
New method reduces FL communication cost by leveraging server's side information.
problem High communication cost in federated learning.
method Exploits server's side information to compress client updates.
result Up to 82 times smaller bitrate with higher accuracy.
Client appraisal improves efficiency in microfinance banks in Adamawa State.
problem Increasing loan defaults and losses in microfinance institutions.
method Survey method with primary and secondary data collection, multi-stage sampling, questionnaires, descriptive and inferential statistics.
result Client appraisal positively affects efficiency and productivity.
FedShuffle improves local updates in FL, especially with data imbalance.
problem Data imbalance in FL leads to different clients performing different numbers of local updates.
method FedShuffle incorporates random reshuffling, data imbalance, and client sampling.
result FedShuffle improves upon FL methods that assume homogeneous updates in heterogeneous setups.
Federated learning studies separate client data and distribution gaps.
problem Understanding performance differences in federated learning across different datasets.
method Proposed a framework to disentangle out-of-sample and participation gaps.
result Dataset synthesis strategy is crucial for realistic simulations of federated learning generalization.
A new method balances model quality and Byzantine robustness in Federated Learning.
problem Byzantine clients sending arbitrary or malicious information.
method Practical weight-truncation-based preprocessing method.
result Empirically demonstrates good balance between model quality and Byzantine robustness.
Study addresses covariate mismatch in federated learning, improving model accuracy.
problem Learning from clients with different feature sets in federated learning.
method Developed two approaches for linear prediction under covariate mismatch: plug-in estimator and impute-then-regress strategy.
result Proposed methods provide asymptotic and finite-sample learning rates, improving model accuracy.
Federated Averaging (FedAvg) has emerged as the algorithm of choice for federated learning due to its simplicity and low communication cost. However, in spite of recent research efforts, its performance is not fully understood. We obtain tight convergence rates for FedAvg and prove that it suffers from `client-drift' w…
SCAFFLSA reduces communication complexity for federated learning with heterogeneous clients.
problem Quantifying and reducing communication complexity in federated learning with heterogeneous clients.
method Proposes SCAFFLSA, a variant of FedLSA using control variates to correct for client drift.
result SCAFFLSA achieves logarithmic communication complexity for statistically heterogeneous agents, scaling with the inverse of the desired accuracy.
New algorithms improve signal processing in federated learning.
problem Efficiently process distributed signal samples with privacy and communication constraints.
method Proposes overpredictive signal approximations using convex optimization.
result Quantifies tradeoffs between communication cost, sampling rate, and approximation error.
Paper proposes a robust method for federated ICA with geometric median aggregation.
problem Federated ICA with permutation ambiguity in client estimations.
method Geometric median aggregation with k-means clustering to resolve permutation ambiguity.
result The method provably remains effective in highly heterogeneous scenarios.
Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and communication bandwidth restrictions. Federated learning is an effective way of training a machine learning model in a distributed manner from l…
Bayesian method improves Federated Learning robustness against corrupted updates.
problem Adversarial attacks on Federated Learning models with unknown number of compromised clients.
method Adaptive Bayesian aggregation based on likelihood of clients being honest.
result Consistently achieves state-of-the-art performance across various attack types.
A new federated learning method speeds up training by selecting faster nodes first.
problem System heterogeneity and stragglers slow down federated learning.
method Adaptive selection of nodes based on data statistical characteristics.
result Significant speedup in wall-clock time compared to standard federated learning.
Client adaptation improves federated learning performance with non-IID data.
problem Improving model performance in federated learning with non-identically and non-independently distributed data.
method Simulates heterogeneous clients to learn client-specific conditioning using a conditional gated activation unit.
result Client adaptation enhances model performance across balanced and imbalanced data sets from audio and image domains.
Ubiquitous systems with End-Edge-Cloud architecture are increasingly being used in healthcare applications. Federated Learning (FL) is highly useful for such applications, due to silo effect and privacy preserving. Existing FL approaches generally do not account for disparities in the quality of local data labels. Howe…
This paper analyzes deep federated learning for low-dimensional data, revealing intrinsic dimensionality's role in convergence rates.
problem Insufficient investigation of generalization error in heterogeneous federated learning, especially for low-dimensional data.
method Statistical analysis of deep federated regression in a two-stage sampling model.
result Intrinsic dimensionality, characterized by entropic dimension, determines convergence rates for deep learners.
We compare communication efficiencies of two compelling distributed machine learning approaches of split learning and federated learning. We show useful settings under which each method outperforms the other in terms of communication efficiency. We consider various practical scenarios of distributed learning setup and …
Boosting-inspired framework for federated learning with progressive model personalization.
problem Statistical heterogeneity across clients in federated learning.
method Construct an ensemble of personalized models, progressively increasing the depth of the personalized component while controlling its effective complexity.
result Consistently outperforms state-of-the-art PFL methods under heterogeneous data distributions.
In this work, we propose a novel framework for privacy-preserving client-distributed machine learning. It is motivated by the desire to achieve differential privacy guarantees in the local model of privacy in a way that satisfies all systems constraints using asynchronous client-server communication and provides attrac…
This paper analyzes and improves convergence in federated learning with biased client selection.
problem Analyzing convergence in federated learning with biased client selection.
method First convergence analysis of federated optimization for biased client selection strategies, proposing Power-of-Choice framework.
result Power-of-Choice strategies converge up to 3 times faster and give 10% higher test accuracy than random selection.
This paper optimizes brokerage contracts for multiple clients trading a single asset.
problem Optimizing brokerage contracts for multiple clients trading a single asset.
method Endogenously determines clients' reservation values and strategically chooses clients. Characterizes optimal portfolios computationally.
result Characterizes optimal portfolios of clients and their profits, showing dependence on price impact coefficients.
Decentralized learning achieves centralized performance via Gibbs measures.
problem Achieving centralized performance in decentralized machine learning.
method ERM-RER learning framework with Gibbs measures and relative-entropy regularization.
result Achieving centralized performance with Gibbs measures and specific scaling of regularization factors.
Personalized federated learning for diverse client objectives.
problem Training a single global model across diverse local datasets is not optimal.
method Efficiently calculates optimal weighted model combinations for each client based on their specific objectives.
result Our method outperforms existing alternatives and enables new personalized features.
Optimal privacy and accuracy in distributed mean estimation with compression.
problem Achieving optimal accuracy under privacy and communication constraints.
method Compression to reduce communication while maintaining privacy and accuracy.
result Achieves optimal error with significantly reduced communication.
We certify federated learning model performance under meta-distribution shifts.
problem Certifying model performance on unseen networks with heterogeneous distributions.
method Derive worst-case uniform guarantees for federated learning model's average loss and risk CDF.
result Asymptotically minimax optimal and privacy-preserving certification.
Bank transactions help predict macroeconomic indexes faster and more accurately.
problem Lag in macroeconomic index availability and autoregressive models' limitations in complex scenarios.
method Use financial transactions data to estimate macroeconomic indexes using neural networks and smart sampling.
result Neural network approach outperforms baseline methods on hand-crafted features based on transactions.
New bounds on learning shared representations improve model performance and efficiency.
problem Improving model performance and efficiency through shared representations across clients.
method Established new upper and lower bounds on statistical error, designed a spectral estimator for non-convex least-squares solutions.
result Optimal statistical rate achieved when shared representation is well covered across clients.
A new scheme for private computation splits client data into shares for server operations.
problem Private computation between client and server without revealing data.
method Stochastic scheme splitting client data into privatized shares.
result Server performs operations on privatized shares without learning raw data.
Federated learning is a recent advance in privacy protection. In this context, a trusted curator aggregates parameters optimized in decentralized fashion by multiple clients. The resulting model is then distributed back to all clients, ultimately converging to a joint representative model without explicitly having to s…
A federated minimax framework for heterogeneous clients.
problem Training with edge devices having different datasets and capabilities.
method Proposes a federated minimax optimization framework with normalized updates.
result Improves convergence and communication complexity for nonconvex functions.
A framework for federated adversarial learning with convergence analysis.
problem Unique vulnerabilities to adversarial attacks in federated learning.
method Formulates a general federated adversarial learning framework with inner and outer loops for client-side adversarial training and server-side model aggregation.
result The minimum loss under the proposed algorithm can converge to ε with chosen learning rate and communication rounds.
Paper proposes FMAB framework for federated learning with two models: approximate and exact.
problem Uncertainty in client sampling and suboptimality gap in federated multi-armed bandits.
method Developed a general FMAB framework and two specific models (approximate and exact), proposing Fed2-UCB for the approximate model.
result Achieved O(log(T)) regret in the approximate model and order-optimal regret in the exact model.
Entropy regularization improves sparse model discovery in federated learning.
problem Sparse model discovery in federated learning with limited data.
method Entropy regularization of gate distributions for probabilistic sparse model exploration.
result Entropy regularization leads to better sparse model recovery and performance.
FMP sampling improves model calibration without sharing data.
problem Difficulties in specifying priors for modern neural networks.
method FMP sampling replaces prior and likelihood with a predictive distribution, running the sampler centrally.
result FMP sampling significantly improves calibration over baseline methods.
Federated framework learns causal states to predict counterfactuals without centralizing data.
problem Decentralized counterfactual reasoning in coupled industrial systems with private data.
method Federated causal representation learning in state-space systems.
result Proves convergence to centralized oracle and provides privacy guarantees.
Unified Bayesian framework for clustered federated learning improves model performance.
problem Handling non-IID client data in federated learning.
method A unified Bayesian framework for clustered federated learning that associates clients to clusters and proposes practical algorithms for data associations.
result The proposed framework increases model performance by circumventing the need for unique client-cluster associations.
FLowDUP trains personalized models with unlabeled clients in low dimensions.
problem Training personalized models on clients with only unlabeled data.
method FLowDUP uses a forward pass with unlabeled data and a transductive PAC-Bayesian bound to generate personalized models in a low-dimensional subspace.
result FLowDUP enables efficient communication and computation with personalized models generated from unlabeled data.