New strategies reduce FL's impact on client resources, enabling larger models and more users.
problem Communication bottleneck in Federated Learning on heterogeneous edge networks.
method Lossy compression and Federated Dropout to reduce client-to-server communication and local computation.
result Up to 14x reduction in server-to-client communication, 1.7x reduction in local computation, and 28x reduction in upload communication.
DaringFed incentivizes clients in OFL with dynamic rewards under TII.
problem Designing incentives for OFL clients under dynamic, incomplete information.
method Formulated as a dynamic signaling and pricing allocation problem in a Bayesian persuasion game.
result Optimal design of DaringFed improves accuracy and convergence speed by 16.99%.
TiFL divides clients into tiers to improve federated learning performance.
problem Heterogeneity in resource and data quality impacts FL performance.
method TiFL divides clients into tiers based on training performance and selects clients from the same tier in each training round.
result TiFL achieves faster training performance with comparable or better test accuracy.
Federated Learning helps IoT devices learn without central servers.
problem Resource constraints in IoT devices hinder traditional ML approaches.
method Local training of IoT devices using global models.
result Federated Learning can be applied to IoT devices with varying resource capabilities.
FAVANO improves federated learning for resource-constrained environments.
problem Asynchronous communication in federated learning leads to bias and scalability issues.
method FAVANO is a novel asynchronous federated learning framework for resource-constrained environments.
result FAVANO outperforms existing methods on standard benchmarks.
Optimal securities lending mechanism incentivizes truthfulness and privacy.
problem Maximizing resource usage in securities lending while ensuring truthful reporting and privacy.
method Bayesian optimal algorithm adapted for differential privacy, combined with market equilibrium dynamics.
result An algorithm that is simultaneously private, approximately optimal, and approximately dominant-strategy truthful.
UDJ-FL framework achieves multiple distributive justice-based fairness metrics in federated learning.
problem Ensuring fairness in federated learning across different client data distributions.
method UDJ-FL framework uses aleatoric uncertainty-based client weighing and fair resource allocation techniques.
result UDJ-FL achieves egalitarian, utilitarian, Rawls' difference principle, and desert-based fairness metrics.
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.
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.
Range penalization enhances statistical accuracy and resource efficiency in federated learning.
problem Statistical accuracy and resource efficiency in federated learning.
method Range regularization and polar clustering.
result Enhanced statistical accuracy and reduced iteration complexity.
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.
Machine learning methods are widely used for a variety of prediction problems. \emph{Prediction as a service} is a paradigm in which service providers with technological expertise and computational resources may perform predictions for clients. However, data privacy severely restricts the applicability of such services…
Hybrid-FL improves ML model accuracy in non-IID data environments.
problem Performance degradation in FL due to non-IID data.
method Hybrid-FL combines client and server learning, selecting optimal clients and data for aggregation.
result 13.5% higher classification accuracy than previous methods.
Client-based machine learning uses mobile devices for computation, improving privacy and reducing data upload.
problem Exploiting mobile devices for machine learning tasks to protect privacy and reduce data upload.
method Leveraging local hardware and data on mobile devices for computation-intensive tasks, only uploading results.
result Client-based machine learning can relieve server burdens and protect user privacy.
This work makes federated Bayesian learning differentially private.
problem Privacy concerns in federated learning with diverse data and computational constraints.
method Modified Partitioned Variational Inference (PVI) to ensure differential privacy.
result Moderately private logistic regression models can be learned in the federated setting with similar performance to non-privately trained models.
This paper tackles federated incremental learning with dynamic memory allocation for improved model performance in non-IID data.
problem Catastrophic forgetting in federated healthcare systems with non-IID data.
method Dynamic memory allocation strategy based on data replay mechanism.
result Significant performance improvements in medical image datasets compared to baseline models.
CSE-FSL reduces communication and storage costs in federated learning.
problem High communication and storage costs in federated learning.
method CSE-FSL uses an auxiliary network to locally update client models and sends only selected epochs' smashed data.
result Significant communication reduction with state-of-the-art convergence and model accuracy.
QFlow learns to prioritize video streaming to improve quality of experience.
problem Inconsistent video streaming quality due to network inefficiency.
method Develops a learning approach to dynamically allocate resources for video streaming.
result Demonstrates improved video quality for all clients at a wireless access point.
Secure submodel learning protects privacy in federated learning.
problem Efficiency and privacy in federated learning for resource-constrained clients.
method Designing a secure federated submodel learning scheme with randomized response, secure aggregation, and Bloom filter.
result Demonstrated the feasibility and scalability of the scheme with practical evaluations.
A new approach corrects bias in federated learning due to varying communication links.
problem Bias in federated learning due to non-uniform and time-varying communication links.
method Proposes Federated Postponed Broadcast (FedPBC) to correct bias in Federated Average (FedAvg).
result FedPBC converges to a stationary point of the global objective, overcoming bias caused by varying communication links.
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.
NAC-FL optimizes model updates in FL systems by adapting compression to network congestion.
problem Federated Learning systems face congestion and delays in data exchanges.
method NAC-FL dynamically adjusts client compression based on network congestion.
result NAC-FL reduces training time and achieves robust performance improvements.
FedFisher improves one-shot FL by using Fisher information.
problem Reducing communication rounds in federated learning.
method Bayesian perspective, Fisher information matrices, diagonal Fisher, K-FAC approximation.
result FedFisher achieves vanishingly small error in two-layer neural networks.
Enhances Federated Learning by prioritizing device contributions and adjusting aggregation parameters.
problem Data privacy and security in distributed learning systems.
method Integrates multiple criteria for device contribution, uses prioritized aggregation, and adapts aggregation parameters online.
result The proposed approach outperforms standard Federated Learning methods in distributed learning tasks.
Optimizes resource allocation in a network with random job requests.
problem Minimizing costs while satisfying job requests within a budget.
method Formalizes as a repeated game, proposes an online saddle-point algorithm.
result Upper bounds for regret and constraint violations are derived.
Paper introduces REED for noncoherent OTA-FL, reducing latency without phase alignment.
problem Noncoherent OTA-FL requires signed model updates without phase alignment.
method Introduces REED for continuous signed aggregation using resource-element energy difference.
result Exact variance laws for REED and chip-diverse extension in Rayleigh fading.
Federated survival analysis outperforms local and centralized training, with RSF offering the best balance of discrimination, calibration, and robustness.
problem Survival analysis models require large, diverse cohorts but are limited by privacy regulations and lack of centralized data.
method Federated learning (FL) is used to train shared models without exchanging raw data.
result FL consistently outperforms local training and approaches, and occasionally exceeds centralized performance.
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.
Study proposes BFEL framework for privacy-preserving FL in personalized healthcare.
problem Privacy and security concerns in traditional cloud-centric ML, especially in wearable devices.
method Develops a blockchain-enhanced federated edge learning (BFEL) framework based on FedCurv, incorporating fisher information matrix and public key encryption.
result Significant reduction in communication cost and high efficiency for federated training on non-iid and heterogeneous data.
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.
Active Federated Learning selects clients to maximize efficiency.
problem Minimizing bandwidth usage and maximizing model accuracy in federated learning.
method Clients are selected with a probability conditioned on the current model and client data to maximize efficiency.
result Reduces the number of required training iterations by 20-70% while maintaining the same model accuracy.
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.
ConvMFiT classifies client utterances in counseling dialogues.
problem Classifying client utterances for better counseling outcomes.
method Fine-tuning a pre-trained conversation model on counselor-client dialogues.
result ConvMFiT outperforms state-of-the-art models in classification.
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.
This work detects anomalous clients in federated learning to prevent their adverse impacts.
problem Detecting and preventing anomalous client behaviors in federated learning systems.
method Generates low-dimensional surrogates of model weight vectors and uses them for anomaly detection.
result The proposed detection-based approach significantly outperforms conventional defense-based methods.
FedCoin uses blockchain to fairly distribute incentives in federated learning.
problem Fairly incentivizing data owners in federated learning with privacy concerns.
method FedCoin uses a blockchain-based peer-to-peer payment system with a proof of Shapley (PoSap) protocol to calculate and distribute Shapley Values.
result FedCoin accurately computes Shapley Values and promotes high-quality data contributions.
Survey of privacy-preserving distributed deep learning methods.
problem Protecting confidential patterns in data during distributed deep learning.
method Comparison of federated learning, split learning, large batch SGD, and privacy-preserving techniques.
result Trade-offs between computational resources, data leakage, and communication efficiency.
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.
Robo-advisor improves investment advice through client interaction.
problem Offering personalized financial advice to clients.
method Adaptive mean-variance portfolio optimization with client interaction.
result Optimal investment strategy includes both myopic and intertemporal hedging terms.
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.
Federated learning algorithm improves with intermittent client availability.
problem Performance degradation in Federated Averaging due to client availability changes.
method Federated Latest Averaging (FedLaAvg) uses latest gradients from all clients, even when unavailable.
result FedLaAvg achieves sublinear speedup compared to classical Federated Averaging.
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.
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.