LOSCAR-SGD combines local updates, sparse model averaging, and communication-computation overlap with a delay-corrected merge rule, providing convergence rates for smooth non-convex objectives under worker heterogeneity.
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A field guide to federated optimization
12 Pith papers cite this work. Polarity classification is still indexing.
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Ringmaster LMO extends delay-thresholding from ASGD to LMO-based momentum updates, providing convergence guarantees under (L0, L1)-smoothness and time-complexity bounds that recover optimal rates in the Euclidean case.
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.
Unified convergence rates and tight lower bounds for Byzantine-robust distributed SGD under stochasticity and general data heterogeneity, showing local momentum reduces stochastic error floors.
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.
DisAgg distributes secure aggregation to a client committee via secret sharing, eliminating local masking and homomorphic encryption while preserving privacy and delivering 4.6x speedup over OPA for 100k clients and 100k-dimensional updates.
Decoupled DiLoCo enables asynchronous distributed pre-training with zero global downtime under simulated failures while preserving competitive performance on text and vision tasks.
ABC-DFL replaces central FL servers with a permissioned blockchain and introduces FLECA, a hierarchical filtering-and-clustering aggregation protocol that keeps convergence close to FedProx while limiting attack impact below 0.10.
Rennala MVR improves time complexity over Rennala SGD for smooth nonconvex stochastic optimization in heterogeneous parallel systems under a mean-squared smoothness assumption.
Entropy regularization of probabilistic gates improves test performance and sparsity recovery in scarce-data federated learning over Fed-IHT and FedAvg pruning.
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.
citing papers explorer
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LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
LOSCAR-SGD combines local updates, sparse model averaging, and communication-computation overlap with a delay-corrected merge rule, providing convergence rates for smooth non-convex objectives under worker heterogeneity.
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Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method
Ringmaster LMO extends delay-thresholding from ASGD to LMO-based momentum updates, providing convergence guarantees under (L0, L1)-smoothness and time-complexity bounds that recover optimal rates in the Euclidean case.
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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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Byzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Bounds
Unified convergence rates and tight lower bounds for Byzantine-robust distributed SGD under stochasticity and general data heterogeneity, showing local momentum reduces stochastic error floors.
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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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DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
DisAgg distributes secure aggregation to a client committee via secret sharing, eliminating local masking and homomorphic encryption while preserving privacy and delivering 4.6x speedup over OPA for 100k clients and 100k-dimensional updates.
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Decoupled DiLoCo for Resilient Distributed Pre-training
Decoupled DiLoCo enables asynchronous distributed pre-training with zero global downtime under simulated failures while preserving competitive performance on text and vision tasks.
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Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
ABC-DFL replaces central FL servers with a permissioned blockchain and introduces FLECA, a hierarchical filtering-and-clustering aggregation protocol that keeps convergence close to FedProx while limiting attack impact below 0.10.
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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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Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning
Entropy regularization of probabilistic gates improves test performance and sparsity recovery in scarce-data federated learning over Fed-IHT and FedAvg pruning.
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A Survey on Foundation Models for Personalized Federated Intelligence
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.
- FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules