Inkheart SGD and M4 use bidirectional compression to achieve time complexities in distributed SGD that improve with worker count n and surpass prior lower bounds under a necessary structural assumption.
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Adaptive federated optimization
11 Pith papers cite this work. Polarity classification is still indexing.
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FedQueue predicts per-facility queue delays, buffers late arrivals via cutoffs, and uses staleness-aware aggregation to achieve O(1/sqrt(R)) convergence and 20.5% real-world improvement in cross-facility HPC federated learning.
Rennala MVR improves time complexity over Rennala SGD for smooth nonconvex stochastic optimization in heterogeneous parallel systems under a mean-squared smoothness assumption.
SPEAR enables online federated LLM fine-tuning by using feedback-guided self-play to create contrastive pairs trained with maximum likelihood on correct completions and confidence-weighted unlikelihood on incorrect ones, outperforming baselines without ground-truth contexts.
FedFrozen improves stability in heterogeneous federated Transformer training by warming up the full model then freezing the attention kernel (query/key) while optimizing the value block under a fixed kernel.
FMCL performs one-shot class-aware client clustering in heterogeneous federated learning by deriving semantic signatures from foundation model embeddings and using cosine distance, yielding improved performance and stable clusters compared to prior methods.
PubSwap uses a small public dataset for selective off-policy response swapping in federated RLVR to improve coordination and performance over standard baselines on math and medical reasoning tasks.
FedInit uses reverse personalized initialization in FL to reduce client drift effects, showing via excess risk that inconsistency impacts generalization error more than optimization error.
Decentralized optimization can reach optimal solutions in fewer iterations than centralized methods for machine learning tasks.
Federated aggregation strategies show distinct performance trade-offs in accuracy, loss, and efficiency depending on whether client data distributions are homogeneous or heterogeneous.
Hierarchical federated learning for plant-disease classification shows distinct accuracy-versus-energy trade-offs across EfficientNet-B0, ResNet-50, and MobileNetV3-Large paired with FedAvg, FedProx, and FedAvgM.
citing papers explorer
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Scalable Distributed Stochastic Optimization via Bidirectional Compression: Beyond Pessimistic Limits
Inkheart SGD and M4 use bidirectional compression to achieve time complexities in distributed SGD that improve with worker count n and surpass prior lower bounds under a necessary structural assumption.
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FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
FedQueue predicts per-facility queue delays, buffers late arrivals via cutoffs, and uses staleness-aware aggregation to achieve O(1/sqrt(R)) convergence and 20.5% real-world improvement in cross-facility HPC federated learning.
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Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
Rennala MVR improves time complexity over Rennala SGD for smooth nonconvex stochastic optimization in heterogeneous parallel systems under a mean-squared smoothness assumption.
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Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedback
SPEAR enables online federated LLM fine-tuning by using feedback-guided self-play to create contrastive pairs trained with maximum likelihood on correct completions and confidence-weighted unlikelihood on incorrect ones, outperforming baselines without ground-truth contexts.
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FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing
FedFrozen improves stability in heterogeneous federated Transformer training by warming up the full model then freezing the attention kernel (query/key) while optimizing the value block under a fixed kernel.
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FMCL: Class-Aware Client Clustering with Foundation Model Representations for Heterogeneous Federated Learning
FMCL performs one-shot class-aware client clustering in heterogeneous federated learning by deriving semantic signatures from foundation model embeddings and using cosine distance, yielding improved performance and stable clusters compared to prior methods.
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PubSwap: Public-Data Off-Policy Coordination for Federated RLVR
PubSwap uses a small public dataset for selective off-policy response swapping in federated RLVR to improve coordination and performance over standard baselines on math and medical reasoning tasks.
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Rethinking the Personalized Relaxed Initialization in the Federated Learning: Consistency and Generalization
FedInit uses reverse personalized initialization in FL to reduce client drift effects, showing via excess risk that inconsistency impacts generalization error more than optimization error.
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Accelerating Optimization and Machine Learning through Decentralization
Decentralized optimization can reach optimal solutions in fewer iterations than centralized methods for machine learning tasks.
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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.
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Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification
Hierarchical federated learning for plant-disease classification shows distinct accuracy-versus-energy trade-offs across EfficientNet-B0, ResNet-50, and MobileNetV3-Large paired with FedAvg, FedProx, and FedAvgM.