Search Results for author: Hossein S. Ghadikolaei

Found 9 papers, 0 papers with code

A-LAQ: Adaptive Lazily Aggregated Quantized Gradient

no code implementations31 Oct 2022 Afsaneh Mahmoudi, José Mairton Barros Da Silva Júnior, Hossein S. Ghadikolaei, Carlo Fischione

This paper proposes Adaptive Lazily Aggregated Quantized Gradient (A-LAQ), which is a method that significantly extends LAQ by assigning an adaptive number of communication bits during the FL iterations.

Federated Learning

Cost-efficient SVRG with Arbitrary Sampling

no code implementations1 Jan 2021 Hossein S. Ghadikolaei, Thomas Ohlson Timoudas, Carlo Fischione

We show that our approach can substantially outperform vanilla SVRG and its variants in terms of both convergence rate and total cost of running the algorithm.

Distributed Optimization

Learning-based Load Balancing Handover in Mobile Millimeter Wave Networks

no code implementations3 Nov 2020 Sara Khosravi, Hossein S. Ghadikolaei, Marina Petrova

Millimeter-wave (mmWave) communication is a promising solution to the high data rate demands in the upcoming 5G and beyond communication networks.

Management

Learning-based Handover in Mobile Millimeter-wave Networks

no code implementations24 Mar 2020 Sara Khosravi, Hossein S. Ghadikolaei, Marina Petrova

We show that our method provides high rate and reliability in all locations of the user's trajectory with a minimal number of handovers.

A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in mmWave Cellular Networks

no code implementations19 Mar 2020 Hossein S. Ghadikolaei, Hadi Ghauch, Gabor Fodor, Mikael Skoglund, Carlo Fischione

Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization and providing a larger bandwidth to individual user equipment at the expense of increasing inter-operator interference.

Communication-efficient Variance-reduced Stochastic Gradient Descent

no code implementations10 Mar 2020 Hossein S. Ghadikolaei, Sindri Magnusson

Moreover, it is much more robust to quantization (in terms of maintaining the true minimizer and the convergence rate) than the state-of-the-art algorithms for solving distributed optimization problems.

Distributed Optimization Quantization

Efficient Beamforming for Mobile mmWave Networks

no code implementations23 Dec 2019 Sara Khosravi, Hossein S. Ghadikolaei, Marina Petrova

We design a lightweight beam-searching algorithm for mobile millimeter-wave systems.

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