TallyTrain is a hard-label distillation protocol for federated learning that uses argmax transmission and optional sparse merges to match soft-label performance at up to 1000x lower communication cost.
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Advances and open problems in federated learning
17 Pith papers cite this work. Polarity classification is still indexing.
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abstract
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.
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Logit-based federated learning leaks private model information to a semi-honest server via shared logits even with unrelated public data, enabling an adaptive stealing attack with theoretical bounds and a logit-perturbation defense.
Provides tight convergence analyses for EF and EF21 error feedback algorithms in distributed optimization, recovering single-agent rates independently of agent count.
The FedSurg challenge benchmarks federated learning on appendectomy videos and finds only 26% F1 on unseen centers even with centralized data, plus extra penalties from decentralization, with spatiotemporal models performing best.
Neural networks trained on scalar diffusion data predict adaptive coarse basis functions for Schwarz methods, transferring without retraining to linear elasticity and nonlinear p-Laplace problems.
An analytical expected-gain score from calibrated posteriors and classwise reliability estimates decides escalation in VFL, improving communication-accuracy trade-off over baselines.
Proposes a covariance-aware tuning-free shrinkage framework and sequential algorithm for multi-source estimation that attains oracle risk asymptotically and improves on single-step methods.
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.
Compass decomposes multi-query multi-SLO planning for compound AI serving, exploits plan similarities, uses selective profiling, and applies bipartite matching at runtime to deliver 2.4-5.1x higher goodput and 3.8-4.5x lower costs.
Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.
An index-aided scheme uses EncGAN to produce random-looking yet ML-usable indices over AES-encrypted data, with small accuracy drops on image classification.
A latency-driven scheduler jointly tunes activation compression and synchronization frequency in federated split learning, delivering up to 87% payload reduction and 54% less synchronization traffic on rainfall prediction with AUPRC change under 0.011.
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
LADSG is a unified defense framework that reduces success rates of passive, active, and direct label inference attacks in VFL by 30-60% via label anonymization, gradient substitution, and norm-based filtering.
BoBa uses data distribution inference and overlapping clustering with voting to detect backdoor attacks in non-IID federated learning, claiming attack success rates below 0.001.
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.
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.
citing papers explorer
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TallyTrain: Communication-Efficient Federated Distillation
TallyTrain is a hard-label distillation protocol for federated learning that uses argmax transmission and optional sparse merges to match soft-label performance at up to 1000x lower communication cost.
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Quantifying and Defending against the Privacy Risk in Logit-based Federated Learning
Logit-based federated learning leaks private model information to a semi-honest server via shared logits even with unrelated public data, enabling an adaptive stealing attack with theoretical bounds and a logit-perturbation defense.
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A Tight Theory of Error Feedback Algorithms in Distributed Optimization
Provides tight convergence analyses for EF and EF21 error feedback algorithms in distributed optimization, recovering single-agent rates independently of agent count.
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Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
The FedSurg challenge benchmarks federated learning on appendectomy videos and finds only 26% F1 on unseen centers even with centralized data, plus extra penalties from decentralization, with spatiotemporal models performing best.
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Learning Adaptive Coarse Spaces Using Transferable Neural Network Models for Linear and Nonlinear Overlapping Domain Decomposition Methods
Neural networks trained on scalar diffusion data predict adaptive coarse basis functions for Schwarz methods, transferring without retraining to linear elasticity and nonlinear p-Laplace problems.
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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
Proposes a covariance-aware tuning-free shrinkage framework and sequential algorithm for multi-source estimation that attains oracle risk asymptotically and improves on single-step methods.
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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
Compass decomposes multi-query multi-SLO planning for compound AI serving, exploits plan similarities, uses selective profiling, and applies bipartite matching at runtime to deliver 2.4-5.1x higher goodput and 3.8-4.5x lower costs.
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Adaptive Federated Optimization
Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.
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MLQENABLER: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing
An index-aided scheme uses EncGAN to produce random-looking yet ML-usable indices over AES-encrypted data, with small accuracy drops on image classification.
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Adaptive Joint Compression and Synchronisation in Federated Split Learning for IoT Rainfall Prediction
A latency-driven scheduler jointly tunes activation compression and synchronization frequency in federated split learning, delivering up to 87% payload reduction and 54% less synchronization traffic on rainfall prediction with AUPRC change under 0.011.
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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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LADSG: Label-Anonymized Distillation and Similar Gradient Substitution for Label Privacy in Vertical Federated Learning
LADSG is a unified defense framework that reduces success rates of passive, active, and direct label inference attacks in VFL by 30-60% via label anonymization, gradient substitution, and norm-based filtering.
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BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
BoBa uses data distribution inference and overlapping clustering with voting to detect backdoor attacks in non-IID federated learning, claiming attack success rates below 0.001.
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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.