Totoro+ is a DHT-based fully decentralized FL system with locality-aware multi-ring P2P structure, pub/sub forest, and game-theoretic path planning that claims O(log N) hops and 1.2-14x speedup for many concurrent applications on edge nodes.
Applied Federated Learning: Improving Google Keyboard Query Suggestions
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access to the underlying user data. We describe our observations in federated training, compare metrics to live deployments, and present resulting quality increases. In whole, we demonstrate how federated learning can be applied end-to-end to both improve user experiences and enhance user privacy.
representative citing papers
For decentralized secure aggregation with at least U surviving users and at most T colluders, the optimal two-round rates are R1 ≥ 1 and R2 ≥ 1/(U-T-1) when U > T+1, and the task is impossible otherwise.
Federated Q-learning in heterogeneous environments achieves linear speedup in K agents for sampling error but is limited to Θ(E/T) convergence when averaging every E steps, with a two-phase error decay-then-rise behavior in experiments.
The paper derives tight information-theoretic bounds on communication and key rates for secure multi-server aggregation under heterogeneous security constraints and arbitrary collusion, with matching schemes in most regimes and a bounded-gap scheme in the rest.
citing papers explorer
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Totoro$^+$: An Adaptive and Scalable Edge Federated Learning System
Totoro+ is a DHT-based fully decentralized FL system with locality-aware multi-ring P2P structure, pub/sub forest, and game-theoretic path planning that claims O(log N) hops and 1.2-14x speedup for many concurrent applications on edge nodes.
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On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
Federated Q-learning in heterogeneous environments achieves linear speedup in K agents for sampling error but is limited to Θ(E/T) convergence when averaging every E steps, with a two-phase error decay-then-rise behavior in experiments.
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Multi-Server Secure Aggregation with Arbitrary Collusion and Heterogeneous Security Constraints
The paper derives tight information-theoretic bounds on communication and key rates for secure multi-server aggregation under heterogeneous security constraints and arbitrary collusion, with matching schemes in most regimes and a bounded-gap scheme in the rest.