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Advances and Open Problems in Federated Learning
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Advances and Open Problems in Federated Learning
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Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.
Forward citations
Cited by 24 Pith papers
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What's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity
Local SGD provably improves over Mini-batch SGD under bounded second-order heterogeneity in the general convex setting, with nearly tight upper and lower bounds.
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Quantifying and Defending against the Privacy Risk in Logit-based Federated Learning
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A Tight Theory of Error Feedback Algorithms in Distributed Optimization
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Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
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Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference
A two-layer Tsetlin Machine ensemble with gossip-based vote sharing matches centralized accuracy on several benchmarks without exchanging raw data.
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Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption
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Federated Lightweight Fine-Tuning
A federated fine-tuning method transmits only 1,280 latent floats per round and reaches near-FedAvg accuracy by exploiting the exact averaging identity of affine mapping networks.
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Learning Adaptive Coarse Spaces Using Transferable Neural Network Models for Linear and Nonlinear Overlapping Domain Decomposition Methods
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Expected Gain-based Escalation in Vertical Federated Learning
An analytical expected-gain score from calibrated posteriors and classwise reliability estimates decides escalation in VFL, improving communication-accuracy trade-off over baselines.
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Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage
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FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning
FLARE uses adaptive multi-dimensional reputation scores and soft exclusion to improve Byzantine robustness in federated learning by up to 16% over prior methods while handling a new Statistical Mimicry attack.
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Compass: SLO-aware Query Planner for Compound AI Serving at Scale
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Adaptive Federated Optimization
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MLQENABLER: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing
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Adaptive Joint Compression and Synchronisation in Federated Split Learning for IoT Rainfall Prediction
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Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proposes proactive client selection via differentially private mutual information and Potential Federation Loss optimized by simulated annealing to achieve faster, fairer, and more accurate federated models than unifo...
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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.
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SuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-Networks
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LADSG: Label-Anonymized Distillation and Similar Gradient Substitution for Label Privacy in Vertical Federated Learning
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BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
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Understanding Communication Backends in Cross-Silo Federated Learning
Benchmarks of MPI, gRPC, and PyTorch RPC in cross-silo FL plus a new gRPC+S3 hybrid backend deliver up to 3.8x speedup for large-model transmission under realistic network conditions.
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A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards
Survey mapping LLM applications in software quality assurance to established standards including ISO/IEC 12207, ISO 25010, CMMI, and TMM, with case studies, challenges, and future directions.
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