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Found 10 papers, 0 papers with code
Scalable and Reliable Over-the-Air Federated Edge Learning
However, FEEL still suffers from a communication bottleneck due to the transmission of high-dimensional model updates from the clients to the federator.
Self-Duplicating Random Walks for Resilient Decentralized Learning on Graphs
We propose a decentralized algorithm called DECAFORK that can maintain the number of RWs in the graph around a desired value even in the presence of arbitrary RW failures.
Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization
We introduce CYBER-0, the first zero-order optimization algorithm for memory-and-communication efficient Federated Learning, resilient to Byzantine faults.
LoByITFL: Low Communication Secure and Private Federated Learning
Federated Learning (FL) faces several challenges, such as the privacy of the clients data and security against Byzantine clients.
Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises
We propose ByITFL, a novel scheme for FL that provides resilience against Byzantine users while keeping the users' data private from the federator and private from other users.
Maximal-Capacity Discrete Memoryless Channel Identification
Based on this capacity estimator, a gap-elimination algorithm termed BestChanID is proposed, which is oblivious to the capacity-achieving input distribution and is guaranteed to output the DMC with the largest capacity, with a desired confidence.
Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion
Existing literature focuses on private aggregation schemes that tackle the privacy problem in federated learning in settings where all users are connected to each other and to the federator.
Fast and Straggler-Tolerant Distributed SGD with Reduced Computation Load
Considering this model, we construct a novel scheme that adapts both the number of workers and the computation load throughout the run-time of the algorithm.
Nested Gradient Codes for Straggler Mitigation in Distributed Machine Learning
Since the number of stragglers in practice is random and unknown a priori, tolerating a fixed number of stragglers can yield a sub-optimal computation load and can result in higher latency.
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits
We consider the distributed SGD problem, where a main node distributes gradient calculations among $n$ workers.
