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.
Secure XGB for privacy-preserving machine learning in federated learning.
problem Privacy-preserving machine learning in federated learning with practical gradient tree boosting models.
method Secure multi-party computation, distributed model storage, secure permutation protocols.
result Our XGB models provide competitive accuracy and practical performance.
Secure federated learning framework resists adversarial users.
problem Resilience against adversarial (Byzantine) users in federated learning.
method Integrated stochastic quantization, verifiable outlier detection, and secure model aggregation.
result First single-server Byzantine-resilient secure aggregation framework (BREA) for secure federated learning.
Paper proposes a secure protocol for federated learning.
problem Combining robustness, privacy, and security in federated learning.
method Secure two-server protocol for federated learning.
result Offers both input privacy and Byzantine-robustness.
PrivacyFL simulates privacy-preserving federated learning.
problem Ensuring privacy in federated learning environments.
method Extensible, configurable simulator for federated learning.
result PrivacyFL checks feasibility and improves model accuracy.
Survey on security and privacy in decentralized federated learning.
problem New threats in decentralized federated learning due to the removal of the server.
method Thorough security analysis and overview of defense mechanisms.
result Challenges and threats in decentralized federated learning.
Federated learning leaks participant dataset quality even with secure aggregation.
problem Leakage of participant dataset quality in federated learning with secure aggregation.
method Image recognition experiments to infer and attribute dataset quality.
result Relative quality ordering of participants can be inferred and used for various purposes.
Secure SMMD enables data privacy in federated learning.
problem Data privacy in federated learning.
method Homomorphic encryption-based Secure Maximum Mean Discrepancy (SMMD).
result Secure SMMD avoids data leakage and enables effective knowledge transfer.
FastSecAgg improves federated learning security and efficiency.
problem Privacy leakage in federated learning due to model parameter sharing.
method Introduces FastSecAgg, a secure aggregation protocol with FFT-based multi-secret sharing (FastShare).
result Efficient in computation and communication, robust to client dropouts.
Secure federated learning reduces privacy risks with differential privacy and secure multiparty computation.
problem Reverse engineering of private client data from federated learning model parameters.
method Combining differential privacy and secure multiparty computation.
result Improved accuracy of shared models without significant privacy loss.
FedBuff improves federated learning scalability with asynchronous updates.
problem Limited scalability of federated learning with synchronous updates.
method Introduces asynchronous updates (staleness) in federated learning.
result Theoretical analysis shows improved convergence rate with boundedness removed.
A new federated learning framework for handling device heterogeneity.
problem Handling device heterogeneity in federated learning.
method Superquantile-based objective with parameterized levels of conformity, optimized using secure aggregation.
result The optimization algorithm converges to a stationary point.
New federated learning protocols resist Byzantine failures and offer privacy guarantees.
problem Resisting Byzantine failures in federated learning.
method Proposes robust federated learning protocols with optimal statistical rates and privacy guarantees.
result Achieves nearly optimal statistical rates and tight rate in terms of all parameters for strongly convex losses.
Combining differential privacy and federated learning improves data security.
problem Ensuring privacy in distributed machine learning.
method Integrating differential privacy with federated learning protocols.
result Achieved comparable prediction accuracy to non-distributed learning while maintaining strict privacy.
A new federated learning method protects privacy in mobile crowdsensing.
problem Data and model privacy protection in federated extreme gradient boosting for mobile crowdsensing.
method Secret sharing based federated learning architecture FedXGB.
result FedXGB achieves less than 1% accuracy loss while preserving model privacy.
Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfe…
A system for federated learning with private data, adding discrete Gaussian noise and secure aggregation.
problem Training models on private data distributed across devices while ensuring privacy.
method Discretizes data, adds discrete Gaussian noise, and uses secure aggregation to protect privacy.
result Matches the accuracy of central differential privacy with less than 16 bits of precision per value.
Federated Extra-Trees protects privacy while improving machine learning performance.
problem Scattered data and privacy concerns in machine learning.
method Local differential privacy in federated trees model.
result Improved accuracy and robustness in federated machine learning.
Turbo-Aggregate reduces secure aggregation time from quadratic to nearly linear.
problem Quadratic overhead in secure model aggregation for federated learning.
method Multi-group circular strategy, additive secret sharing, and coding techniques.
result Achieves O(NlogN) overhead, compared to O(N2), for up to 50% user dropout. Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing work on federated learning with limited communication demonstrates how …
Secure aggregation for buffered asynchronous federated learning without TEEs.
problem Privacy and convergence in buffered asynchronous federated learning.
method Developed a new protocol (BASecAgg) that ensures privacy without TEEs by carefully designing masks.
result BASecAgg achieves similar convergence guarantees as FedBuff without TEEs.
Federated learning was proposed with an intriguing vision of achieving collaborative machine learning among numerous clients without uploading their private data to a cloud server. However, the conventional framework requires each client to leverage the full model for learning, which can be prohibitively inefficient fo…
QFNN-FFD uses quantum computing and FL for secure financial fraud detection.
problem Financial fraud detection in the financial sector.
method Quantum Federated Neural Network (QFNN-FFD) combining QML and FL.
result Achieves precision rates above 95% and robustness against noise.
New methods reduce private federated learning communication automatically.
problem Reducing communication in private federated learning.
method Automatic compression rate adjustment based on training error, using secure aggregation and differential privacy.
result Provable instance-optimal for mean estimation, achieving favorable compression rates.
A new method for federated learning aggregates data from multiple sites efficiently.
problem Aggregating data from multiple sites securely and effectively.
method Sequential federated learning with distributed computing.
result Preserves information from individual analyses and accelerates the analysis process.
FedGRU uses federated learning to predict traffic flow accurately while preserving user privacy.
problem Developing accurate traffic flow prediction while protecting user privacy.
method Federated Learning, Secure Parameter Aggregation, Joint Announcement Protocol, Ensemble Clustering.
result FedGRU achieves 90.96% higher prediction accuracy than advanced deep learning models.
Our work specifies the fundamental cost of using secure aggregation in federated learning.
problem Training a distributed model with differential privacy constraints.
method Characterized the communication cost required for optimal accuracy under differential privacy, achieved by a linear scheme.
result The fundamental communication cost is $ ilde{O}\left( \min(n^2\varepsilon^2, d)
ight)$ bits per client, both sufficient and necessary.
New study tackles free-rider attacks in federated learning models.
problem Free-rider attacks compromise federated learning models by non-contributing to data updates.
method Theoretical and experimental analysis of iterative federated learning schemes, including FedAvg and FedProx.
result Formal guarantees for free-rider attacks to converge to aggregated models of fair participants.
Proposes a method for private aggregation in heterogeneous federated learning.
problem Ensuring resilience to Byzantine clients and maintaining client data privacy in federated learning with heterogeneous data.
method Careful co-design of verifiable secret sharing, secure aggregation, and private information retrieval scheme.
result Achieves information-theoretic privacy guarantees and Byzantine resilience under data heterogeneity.
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descen…
Collaborative (federated) learning enables multiple parties to train a model without sharing their private data, but through repeated sharing of the parameters of their local models. Despite its advantages, this approach has many known privacy and security weaknesses and performance overhead, in addition to being limit…
A new hybrid federated learning algorithm for combining clinical and omics data.
problem Combining clinical and omics data in federated learning settings.
method Reformulated Kernel Regularized Least Squares algorithm for hybrid federated learning.
result Validation of two variants of the hybrid algorithm on well-established datasets.
Most real-world data are scattered across different companies or government organizations, and cannot be easily integrated under data privacy and related regulations such as the European Union's General Data Protection Regulation (GDPR) and China' Cyber Security Law. Such data islands situation and data privacy & secur…
Paper develops a federated learning method to protect privacy without sacrificing model utility.
problem Privacy leakage in federated learning due to information exchange between edge devices and server.
method Combines local gradient perturbation, secure aggregation, and zCDP for privacy protection.
result Demonstrates superior trade-off between privacy and model utility through extensive experiments.
FTTQ optimizes quantized networks in federated learning, reducing communication costs.
problem Redundant parameters in full-precision models lead to excessive communication costs in federated learning.
method FTTQ algorithm that optimizes quantized networks on clients through self-learning quantization factors.
result FTTQ reduces communication costs and can achieve slightly better performance on non-IID data.
Post-Quantum Secure Federated DeFi for Inclusive Banking
problem Financial systems and DeFi ecosystems are vulnerable to quantum computing threats.
method Post-Quantum Secure Federated DeFi framework using lattice-based FHE.
result End-to-end homomorphic computation enables inter-bank collaboration.
A privacy-preserving framework detects faults in circular economy processes.
problem Lack of shared data across company borders due to privacy concerns.
method Federated Principal Component Analysis (PCA) and Secure Multiparty Computation.
result The proposed FedMSPC framework outperforms standard PCA in fault detection.
Improved fraud detection in finance with quantum-enhanced federated learning.
problem Challenges in detecting financial fraud with traditional methods.
method Hybrid quantum-enhanced federated learning framework combining quantum LSTM with privacy-preserving techniques.
result Approximately 5% improvement in performance metrics compared to conventional models.
Secure sum outperforms homomorphic encryption in collaborative deep learning.
problem Training deep learning models on private data from multiple parties without revealing the data.
method Used a secure sum protocol in conjunction with default secure channels.
result Secure sum protocol provides superior properties in terms of collusion-resistance and runtime.
HDP-VFL hybridizes DP for VFL, reducing privacy costs.
problem Privacy-preserving collaborative learning from vertically partitioned data.
method Hybrid DP framework combining HE and MPC for VFL.
result Achieves DP and JDP with negligible training time and accuracy trade-offs.
FDSKL algorithm trains vertically partitioned data with kernels securely and efficiently.
problem Training vertically partitioned data with kernels while maintaining privacy.
method FDSKL algorithm using random features and doubly stochastic gradients for federated learning.
result FDSKL achieves sublinear convergence and guarantees data security.
A new discrete privacy mechanism for federated learning.
problem Differentially private federated learning with communication constraints.
method Skellam mechanism based on Poisson distributions.
result Skellam mechanism provides similar privacy-accuracy trade-offs as Gaussian mechanism.
The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized…
Fed-FEARE model extracts rules from multiple agencies' data securely.
problem Data privacy and secure rule extraction across multiple agencies.
method Federated F-score based ensemble tree model.
result Model performance measures significantly improved with federated learning.
LightSecAgg reduces secure aggregation complexity in FL.
problem Complexity in secure aggregation protocols for FL systems.
method One-shot aggregate-mask reconstruction via mask encoding/decoding.
result Significantly reduces overhead for resiliency against dropped users.
FLBench automates federated learning benchmarking.
problem Manual dataset partitioning fails to simulate real-world isolated data islands.
method Develops a federated learning benchmark suite with three domains.
result Automates evaluation of federated learning systems and algorithms.
Quantum federated learning improves with non-IID data using one-shot communication.
problem Performance degradation in federated learning with non-IID data.
method Quantum algorithms and local density estimators for non-IID data.
result One-shot communication complexity for non-IID quantum federated learning.
This paper analyzes privacy threats in federated matrix factorization.
problem Privacy threats in federated matrix factorization models.
method Categorizes federated matrix factorization into three types and analyzes privacy threats.
result This is the first study of privacy threats in federated matrix factorization.