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Found 13 papers, 8 papers with code
FedRule: Federated Rule Recommendation System with Graph Neural Networks
Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera triggering a smart lock to unlock a door.
Learning Distributionally Robust Models at Scale via Composite Optimization
To train machine learning models that are robust to distribution shifts in the data, distributionally robust optimization (DRO) has been proven very effective.
Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation
Despite this advancement in different techniques for distilling the knowledge, the aggregation of different paths for distillation has not been studied comprehensively.
Ranked #33 on
Knowledge Distillation
on ImageNet
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time
This work is the first to show the convergence of Local SGD on non-smooth functions, and will shed lights on the optimization theory of federated training of deep neural networks.
Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing
The proposed PEF notion is definition-agnostic, meaning that any well-defined notion of fairness can be reduced to the PEF notion.
Distributionally Robust Federated Averaging
To compensate for this, we propose a Distributionally Robust Federated Averaging (DRFA) algorithm that employs a novel snapshotting scheme to approximate the accumulation of history gradients of the mixing parameter.
Targeted Data-driven Regularization for Out-of-Distribution Generalization
The proposed framework, named targeted data-driven regularization (TDR), is model- and dataset-agnostic and employs a target dataset that resembles the desired nature of test data in order to guide the learning process in a coupled manner.
Federated Learning with Compression: Unified Analysis and Sharp Guarantees
In federated learning, communication cost is often a critical bottleneck to scale up distributed optimization algorithms to collaboratively learn a model from millions of devices with potentially unreliable or limited communication and heterogeneous data distributions.
Adaptive Personalized Federated Learning
Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize.
Efficient Fair Principal Component Analysis
It has been shown that dimension reduction methods such as PCA may be inherently prone to unfairness and treat data from different sensitive groups such as race, color, sex, etc., unfairly.
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
Specifically, we show that for loss functions that satisfy the Polyak-{\L}ojasiewicz condition, $O((pT)^{1/3})$ rounds of communication suffice to achieve a linear speed up, that is, an error of $O(1/pT)$, where $T$ is the total number of model updates at each worker.
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization
Communication overhead is one of the key challenges that hinder the scalability of distributed optimization algorithms to train large neural networks.
CAPTAIN: Comprehensive Composition Assistance for Photo Taking
A user study demonstrates that the work is useful to those taking photos.
