A two-stage optimization framework reduces label noise in federated learning.
problem Label noise from noisy clients degrades federated learning model performance.
method MaskedOptim framework: detects noisy clients, corrects labels, and aggregates models robustly.
result Our framework improves model robustness and data quality in federated learning.
A federated model learns shared archetypes from heterogeneous clients in continual learning.
problem Federated learning struggles with client heterogeneity and streaming distribution shifts.
method Clients encode their data as low-rank Hebbian operators, which are sent to a central server for aggregation and factorization into global archetypes.
result Improved global archetype reconstruction and associative retrieval in heterogeneous clients, drift, and novelty settings.
Proposes HOTA-FedGradNorm for faster and robust PFL in noisy channels.
problem Statistical heterogeneity across clients in federated learning.
method Dynamic weighting, OTA aggregation, hierarchical federated learning.
result Training speed improvement and robustness against channel effects.
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…
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…
A federated method for feature selection in multi-label data.
problem Feature selection in multi-label data for distributed and federated environments.
method Semi-Supervised Federated Multi-Label Feature Selection (SSFMLFS) using fuzzy information measures.
result SSFMLFS outperforms other methods in feature selection for multi-label data in federated settings.
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.
IDA adapts to non-iid data in federated learning for medical imaging.
problem Statistical heterogeneity in federated learning data, especially in medical imaging.
method IDA (Inverse Distance Aggregation) is a novel adaptive weighting approach for clients based on meta-information.
result IDA outperforms Federated Averaging in handling unbalanced and non-iid data in 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.
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.
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.
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.
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.
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.
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 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.
The recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. A dataset of those interactions can be used to learn to automatically classify the client utterances into categories that help counselors in diagnosing client status and predictin…
DFedAvgM is a decentralized FedAvg with momentum for privacy and communication efficiency.
problem Efficiently train models with privacy and communication efficiency in federated learning.
method Decentralized Federated Averaging with Momentum (DFedAvgM) on clients connected by an undirected graph, using stochastic gradient descent with momentum and quantization.
result DFedAvgM converges under trivial assumptions and can be improved with the PŁ property, numerically verified.
In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server. Consequently, a client may intentionally or unintentionally deviate from the prescribed course of federated model training, resulting in abnormal behaviors, such as turning into a malicious attacker or a …
Group personalization improves FL performance in heterogeneous client data.
problem Mitigating client drift in federated learning with heterogeneous data.
method Fine-tuning a global FL model over homogeneous groups of clients, then personalizing each group's model.
result The proposed method achieves superior personalization performance compared to other FL approaches.
FedACS uses attention to select clients with similar data for federated learning.
problem Non-IID data and data scarcity in federated learning.
method FedACS integrates an attention mechanism to prioritize clients with similar data distributions.
result FedACS improves federated learning performance by addressing non-IID data and data scarcity.
Unified analysis of FL with arbitrary client participation.
problem Challenges of intermittent client availability and efficiency in FL.
method Introduces a generalized FedAvg and novel analysis capturing client participation.
result Unified convergence upper bounds for various participation patterns.
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work, we consider a practical and ubiquitous issue when deploying federated learning in mobile environments: intermittent client availability, w…
This paper proposes a cooperative mechanism for mitigating the performance degradation due to non-independent-and-identically-distributed (non-IID) data in collaborative machine learning (ML), namely federated learning (FL), which trains an ML model using the rich data and computational resources of mobile clients with…
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.
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.
Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. The viability of robo-advisors crucially depends on their ability to offer personalized financial advice. We introduce a novel framework, in which a robo-advisor interacts with a client to solve an adaptiv…
A new framework for federated continual learning reduces interference and improves performance.
problem Learning from a sequence of tasks with limited data from each client.
method Federated Weighted Inter-client Transfer (FedWeIT) framework.
result FedWeIT significantly outperforms existing methods with reduced communication cost.
New method for semi-supervised learning in federated learning with and without labels at clients.
problem Training federated learning models with partially or completely unlabeled data.
method Federated Matching (FedMatch) with inter-client consistency loss and disjoint learning.
result FedMatch outperforms local semi-supervised learning and naive federated learning combinations.
A novel incentive mechanism improves fairness and participation in federated learning.
problem Low-quality clients and lack of fairness in federated learning.
method Client selection process and money transfer mechanism to ensure fairness and participation.
result The proposed incentive mechanism improves the duration and fairness of federated learning.
Algorithm optimizes collaborative learning among distributed clients using kernel-based bandits.
problem Optimizing personalized objectives in a distributed system with limited global information.
method Kernel-based bandit framework with surrogate Gaussian process models, sparse approximations.
result Order-optimal regret performance (up to polylogarithmic factors) and reduced communication overhead.
Federated Granger causality learns reliable interactions without sharing data.
problem Uncertainty in federated Granger causality estimates.
method Closed-form covariance recursions and spectral-radius-based convergence conditions.
result Uncertainty depends only on client data statistics and is independent of model parameters.
Study Nash competition among dealers quoting prices to clients with unknown trading motives.
problem Adverse selection and inventory costs in dealer-client interactions.
method Analyzes one-shot Nash competition with unknown client type and inventory constraints.
result Unique symmetric Nash equilibrium exists and can be characterized by a nonlinear ODE.
Personalizes query completion in digital commerce with image-based vectors.
problem High bounce rate and low recurring users in query completion.
method Injects dense vectors from product images into a noisy channel model.
result Image-based personalization improves query completion and user experience.
A simple strategy optimizes broker-client trading, reducing price discounts for informed traders.
problem Optimizing broker-client trading to balance client flow and informed trader losses.
method Modelled as a stochastic control problem, derived optimal strategy in closed form, introduced algorithm.
result Optimal strategy reduces price discounts for informed traders, balancing client flow and informed trader losses.
A new FL algorithm reduces communication overhead by selectively updating model parameters.
problem Data heterogeneity and communication overhead in federated learning.
method Uses age of information metric to selectively update model parameters and group clients with similar data.
result Our method can expedite training and surpass other communication-efficient strategies in efficiency.
HeteroFL trains diverse clients with varying capabilities efficiently.
problem Training models on clients with different computational and communication capacities.
method Proposes HeteroFL framework to adaptively distribute subnetworks based on client capabilities.
result Adaptive subnetwork distribution leads to efficient computation and communication.
Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation. To address this issue, we introduce two novel strategies to reduce communication costs: (1) the use of lossy compression on the global model sent server-to-client;…
Improved Bayesian FL method calibrates predictions for federated learning.
problem Overconfident predictions in Bayesian FL methods for federated learning.
method β-Predictive Bayes algorithm interpolates between mixture and product of local predictive posteriors, tuning parameter β for better calibration.
result Demonstrated superior calibration compared to other baselines, even with increased data heterogeneity.
Federated learning interprets temporal dynamics across clients with graph attention.
problem Interpreting temporal patterns across decentralized, heterogeneous systems with nonlinear dynamics.
method Graph Attention Network for learning state transition models over latent states communicated between clients.
result First interpretable characterization of cross-client temporal interdependencies in decentralized nonlinear systems.
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.
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.
This work introduces reward teaching for federated multi-armed bandits to guide clients towards global optimality.
problem Existing federated multi-armed bandits designs assume clients will follow the server's protocol, but this is not always feasible.
method Introduces reward teaching where the server adjusts clients' local rewards to encourage global optimality, using phased Teaching-After-Learning (TAL) and Teaching-While-Learning (TWL) algorithms.
result Demonstrates that TAL achieves logarithmic regrets with only logarithmic adjustment costs, and TWL outperforms TAL for UCB1 clients.