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.
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
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XFED is the first aggregation-agnostic non-collusive model poisoning attack that bypasses eight state-of-the-art defenses on six benchmark datasets without attacker coordination.
Nexa learns a response-conditioned policy that starts with parallel agent execution and adds at most one round of sequential message passing via a predicted sparse DAG, strictly subsuming pure parallel mode.
FedF-ADMM uses function-space ADMM updates projected via knowledge distillation plus a PI-like stabilization term to deliver faster, more stable convergence and higher accuracy than prior decentralized FL methods under severe non-IID conditions.
FedKDNAS combines client-side neural architecture search with knowledge distillation from aggregated server predictions to improve accuracy and efficiency in heterogeneous federated learning.
A survey organizing knowledge distillation techniques for addressing privacy, heterogeneity, communication, and personalization challenges in federated learning.
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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XFED: Non-Collusive Model Poisoning Attack Against Byzantine-Robust Federated Classifiers
XFED is the first aggregation-agnostic non-collusive model poisoning attack that bypasses eight state-of-the-art defenses on six benchmark datasets without attacker coordination.
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Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems
Nexa learns a response-conditioned policy that starts with parallel agent execution and adds at most one round of sequential message passing via a predicted sparse DAG, strictly subsuming pure parallel mode.
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Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective
FedF-ADMM uses function-space ADMM updates projected via knowledge distillation plus a PI-like stabilization term to deliver faster, more stable convergence and higher accuracy than prior decentralized FL methods under severe non-IID conditions.
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Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search
FedKDNAS combines client-side neural architecture search with knowledge distillation from aggregated server predictions to improve accuracy and efficiency in heterogeneous federated learning.
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Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
A survey organizing knowledge distillation techniques for addressing privacy, heterogeneity, communication, and personalization challenges in federated learning.