Pages that link to "Item:Q5012283"
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The following pages link to Advances and Open Problems in Federated Learning (Q5012283):
Displaying 31 items.
- The more the merrier: reducing the cost of large scale MPC (Q2056763) (← links)
- Superquantiles at work: machine learning applications and efficient subgradient computation (Q2070410) (← links)
- Private aggregation from fewer anonymous messages (Q2119033) (← links)
- FL-MAC-RDP: federated learning over multiple access channels with Rényi differential privacy (Q2239648) (← links)
- SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Q5076321) (← links)
- Proximal Splitting Algorithms for Convex Optimization: A Tour of Recent Advances, with New Twists (Q6046287) (← links)
- Gradient-free federated learning methods with \(l_1\) and \(l_2\)-randomization for non-smooth convex stochastic optimization problems (Q6053598) (← links)
- Finite-time convergence rates of distributed local stochastic approximation (Q6088356) (← links)
- An accurate, scalable and verifiable protocol for federated differentially private averaging (Q6097108) (← links)
- Local2global: a distributed approach for scaling representation learning on graphs (Q6103578) (← links)
- Federated learning for minimizing nonsmooth convex loss functions (Q6112869) (← links)
- Optimal Methods for Convex Risk-Averse Distributed Optimization (Q6116242) (← links)
- Optimal data splitting in distributed optimization for machine learning (Q6124406) (← links)
- SHED: a Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing (Q6152580) (← links)
- Coopetition against an Amazon (Q6164519) (← links)
- Decentralized personalized federated learning: lower bounds and optimal algorithm for all personalization modes (Q6170035) (← links)
- FedHD: communication-efficient federated learning from hybrid data (Q6177550) (← links)
- Network Gradient Descent Algorithm for Decentralized Federated Learning (Q6190693) (← links)
- Faster Rates for Compressed Federated Learning with Client-Variance Reduction (Q6202285) (← links)
- DeFTA: a plug-and-play peer-to-peer decentralized federated learning framework (Q6544587) (← links)
- iDP-FL: a fine-grained and privacy-aware federated learning framework for deep neural networks (Q6576937) (← links)
- Byzantine-resilient decentralized network learning (Q6581395) (← links)
- Max-affine regression via first-order methods (Q6583522) (← links)
- Resource-adaptive Newton's method for distributed learning (Q6591485) (← links)
- Federated learning on non-IID and globally long-tailed data via meta re-weighting networks (Q6591696) (← links)
- Distributed optimal subsampling for quantile regression with massive data (Q6592801) (← links)
- Differentially private federated learning with local momentum updates and gradients filtering (Q6595290) (← links)
- Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies (Q6618513) (← links)
- Implementation of an oracle-structured bundle method for distributed optimization (Q6640181) (← links)
- Core-elements for large-scale least squares estimation (Q6643225) (← links)
- Adaptive pruning-based Newton's method for distributed learning (Q6658297) (← links)
