For decentralized secure aggregation with at least U surviving users and at most T colluders, the optimal two-round rates are R1 ≥ 1 and R2 ≥ 1/(U-T-1) when U > T+1, and the task is impossible otherwise.
hub
Practical Secure Aggregation for Federated Learning on User-Held Data
13 Pith papers cite this work, alongside 190 external citations. Polarity classification is still indexing.
13
Pith papers citing it
190
external citations · Pith
abstract
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 descent across user-held training data on mobile devices, wherein Secure Aggregation protects each user's model gradient. We design a novel, communication-efficient Secure Aggregation protocol for high-dimensional data that tolerates up to 1/3 users failing to complete the protocol. For 16-bit input values, our protocol offers 1.73x communication expansion for $2^{10}$ users and $2^{20}$-dimensional vectors, and 1.98x expansion for $2^{14}$ users and $2^{24}$ dimensional vectors.
hub tools
citation-role summary
background 2
citation-polarity summary
roles
background 2polarities
background 2representative citing papers
The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
FedCoLLM is a parameter-efficient federated co-tuning framework that improves client SLMs via server LLMs and enriches LLMs with client domain insights using adapters on NLP text generation tasks.
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
FedACT schedules devices across concurrent FL jobs via alignment scoring and fairness to reduce average job completion time by up to 8.3x and raise accuracy by up to 44.5% versus baselines.
QuNetQFL is a quantum federated learning protocol using distributed quantum keys for secure aggregation, experimentally validated on a four-client quantum network with scalability simulations to 200 clients and applications to quantum datasets and hybrid language models.
The paper derives tight information-theoretic bounds on communication and key rates for secure multi-server aggregation under heterogeneous security constraints and arbitrary collusion, with matching schemes in most regimes and a bounded-gap scheme in the rest.
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
CPPDD is a new consensus-based protocol for privacy-preserving multi-client data sharing that achieves unanimous-release confidentiality, linear scalability, and high-probability malicious deviation detection.
FedShield-LLM integrates pruning and FHE on LoRA parameters to support secure, scalable federated fine-tuning of LLMs such as Llama-2.
UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.
A system architecture combines GenAI with typed argument graphs, RAG, and deterministic validation rules to generate traceable, evidence-supported formal arguments for regulatory compliance.
Federated aggregation strategies show distinct performance trade-offs in accuracy, loss, and efficiency depending on whether client data distributions are homogeneous or heterogeneous.
citing papers explorer
-
Information-Theoretic Decentralized Secure Aggregation with User Dropouts
For decentralized secure aggregation with at least U surviving users and at most T colluders, the optimal two-round rates are R1 ≥ 1 and R2 ≥ 1/(U-T-1) when U > T+1, and the task is impossible otherwise.
-
Rashomon Sets and Model Multiplicity in Federated Learning
The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
-
Federated Co-tuning Framework for Large and Small Language Models
FedCoLLM is a parameter-efficient federated co-tuning framework that improves client SLMs via server LLMs and enriches LLMs with client domain insights using adapters on NLP text generation tasks.
-
Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
-
FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources
FedACT schedules devices across concurrent FL jobs via alignment scoring and fairness to reduce average job completion time by up to 8.3x and raise accuracy by up to 44.5% versus baselines.
-
Experimentally validated quantum-secure federated learning over a multi-user quantum network
QuNetQFL is a quantum federated learning protocol using distributed quantum keys for secure aggregation, experimentally validated on a four-client quantum network with scalability simulations to 200 clients and applications to quantum datasets and hybrid language models.
-
Multi-Server Secure Aggregation with Arbitrary Collusion and Heterogeneous Security Constraints
The paper derives tight information-theoretic bounds on communication and key rates for secure multi-server aggregation under heterogeneous security constraints and arbitrary collusion, with matching schemes in most regimes and a bounded-gap scheme in the rest.
-
Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
-
Secure, Verifiable, and Scalable Multi-Client Data Sharing via Consensus-Based Privacy-Preserving Data Distribution
CPPDD is a new consensus-based protocol for privacy-preserving multi-client data sharing that achieves unanimous-release confidentiality, linear scalability, and high-probability malicious deviation detection.
-
FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model
FedShield-LLM integrates pruning and FHE on LoRA parameters to support secure, scalable federated fine-tuning of LLMs such as Llama-2.
-
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.
-
Compliance-by-Construction Argument Graphs: Using Generative AI to Produce Evidence-Linked Formal Arguments for Certification-Grade Accountability
A system architecture combines GenAI with typed argument graphs, RAG, and deterministic validation rules to generate traceable, evidence-supported formal arguments for regulatory compliance.
-
A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions
Federated aggregation strategies show distinct performance trade-offs in accuracy, loss, and efficiency depending on whether client data distributions are homogeneous or heterogeneous.