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Federated Optimization in Heterogeneous Networks
TLDR
This work introduces a framework, FedProx, to tackle heterogeneity in federated networks, and provides convergence guarantees for this framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work.
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LEAF: A Benchmark for Federated Settings
TLDR
LEAF is proposed, a modular benchmarking framework for learning in federated settings that includes a suite of open-source federated datasets, a rigorous evaluation framework, and a set of reference implementations, all geared towards capturing the obstacles and intricacies of practical federated environments.
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Federated Learning: Challenges, Methods, and Future Directions
TLDR
The unique characteristics and challenges of federated learning are discussed, a broad overview of current approaches are provided, and several directions of future work that are relevant to a wide range of research communities are outlined.
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Federated Multi-Task Learning
TLDR
This work shows that multi-task learning is naturally suited to handle the statistical challenges of this setting, and proposes a novel systems-aware optimization method, MOCHA, that is robust to practical systems issues.
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Fair Resource Allocation in Federated Learning
TLDR
This work proposes q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by fair resource allocation in wireless networks that encourages a more fair accuracy distribution across devices in federated networks.
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Communication-Efficient Distributed Dual Coordinate Ascent
TLDR
A communication-efficient framework that uses local computation in a primal-dual setting to dramatically reduce the amount of necessary communication is proposed, and a strong convergence rate analysis is provided for this class of algorithms.
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Ditto: Fair and Robust Federated Learning Through Personalization
TLDR
This work identifies that robustness to data and model poisoning attacks and fairness, measured as the uniformity of performance across devices, are competing constraints in statistically heterogeneous networks and proposes a simple, general framework, Ditto, that can inherently provide fairness and robustness benefits.
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On the Convergence of Federated Optimization in Heterogeneous Networks
TLDR
This work proposes and introduces \fedprox, which is similar in spirit to \fedavg, but more amenable to theoretical analysis, and describes the convergence of \fed Prox under a novel \textit{device similarity} assumption.
Adding vs. Averaging in Distributed Primal-Dual Optimization
TLDR
A novel generalization of the recent communication-efficient primal-dual framework (COCOA) for distributed optimization, which allows for additive combination of local updates to the global parameters at each iteration, whereas previous schemes with convergence guarantees only allow conservative averaging.
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MLI: An API for Distributed Machine Learning
TLDR
The initial results show that this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability.
View on IEEE
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