FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud System

in IEEE Transactions on Wireless Communications (in press)

Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of ... [more ▼]

Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of enabling FL over a wireless fogcloud system (e.g., non-i.i.d. data, users’ heterogeneity), we first propose an efficient FL algorithm based on Federated Averaging (called FedFog) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud. Next, we employ FedFog in wireless fog-cloud systems by investigating a novel network-aware FL optimization problem that strikes the balance between the global loss and completion time. An iterative algorithm is then developed to obtain a precise measurement of the system performance, which helps design an efficient stopping criteria to output an appropriate number of global rounds. To mitigate the straggler effect, we propose a flexible user aggregation strategy that trains fast users first to obtain a certain level of accuracy before allowing slow users to join the global training updates. Extensive numerical results using several real-world FL tasks are provided to verify the theoretical convergence of FedFog. We also show that the proposed co-design of FL and communication is essential to substantially improve resource utilization while achieving comparable accuracy of the learning model. [less ▲]

Intelligent Blockchain-based Edge Computing via Deep Reinforcement Learning: Solutions and Challenges

in IEEE Network (in press)

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things networks, by enabling task offloading with security enhancement ... [more ▼]

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things networks, by enabling task offloading with security enhancement based on blockchain mining. Yet the existing approaches for these enabling technologies are isolated, providing only tailored solutions for specific services and scenarios. To fill this gap, we propose a novel cooperative task offloading and blockchain mining (TOBM) scheme for a blockchain-based MEC system, where each edge device not only handles computation tasks but also deals with block mining for improving system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. To accommodate the highly dynamic environment and high-dimensional system state space, we apply a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. Experimental results demonstrate the superior performance of the proposed TOBM scheme in terms of enhanced system reward, improved offloading utility with lower blockchain mining latency, and better system utility, compared to the existing cooperative and non-cooperative schemes. The paper concludes with key technical challenges and possible directions for future blockchain-based MEC research. [less ▲]

Adaptive Beam Pattern Selection and Resource Allocation for NOMA-Based LEO Satellite Systems

Scientific Conference (2022, December 04)

Power Allocation for Space-Terrestrial Cooperation Systems with Statistical CSI

in IEEE Global Communications Conference (IEEE Globecom), Rio de Janeiro, Brazil, Dec. 2022 (2022, December)

Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels

in IEEE Open Journal of the Communications Society (2022)

Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their ... [more ▼]

Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their performance in fulfilling a given task. While in traditional communications the goal has often been to reconstruct the underlying message, under the emerging task-oriented paradigm, the goal of communication is to enable the receiving end to make more informed decisions or more precise estimates/computations. Motivated by these recent developments, in this paper, we perform an indirect design of the communications in a multi-agent system (MAS) in which agents cooperate to maximize the averaged sum of discounted one-stage rewards of a collaborative task. Due to the bit-budgeted communications between the agents, each agent should efficiently represent its local observation and communicate an abstracted version of the observations to improve the collaborative task performance. We first show that this problem can be approximated as a form of data-quantization problem which we call task-oriented data compression (TODC). We then introduce the state-aggregation for information compression algorithm (SAIC) to solve the formulated TODC problem. It is shown that SAIC is able to achieve near-optimal performance in terms of the achieved sum of discounted rewards. The proposed algorithm is applied to a geometric consensus problem and its performance is compared with several benchmarks. Numerical experiments confirm the promise of this indirect design approach for task-oriented multi-agent communications. [less ▲]

Towards the Application of Neuromorphic Computing to Satellite Communications

in Towards the Application of Neuromorphic Computing to Satellite Communications (2022, October)

Artificial intelligence (AI) has recently received significant attention as a key enabler for future 5G-and-beyond terrestrial wireless networks. The applications of AI to satellite communications is also ... [more ▼]

Artificial intelligence (AI) has recently received significant attention as a key enabler for future 5G-and-beyond terrestrial wireless networks. The applications of AI to satellite communications is also gaining momentum to realize a more autonomous operation with reduced requirements in terms of human intervention. The adoption of AI for satellite communications will set new requirements on computing processors, which will need to support large workloads as efficiently as possible under harsh environmental conditions. In this context, neuromorphic processing (NP) is emerging as a bio-inspired solution to address pattern recognition tasks involving multiple, possibly unstructured, temporal signals and/or requiring continual learning. The key merits of the technology are energy efficiency and capacity for on-device adaptation. In this paper, we highlight potential use cases and applications of NP to satellite communications. We also explore major technical challenges for the implementation of space-based NP focusing on the available NP chipsets. [less ▲]

Satellite Beam Densification for High-Demand Areas

in 2022 11th Advanced Satellite Multimedia Systems Conference and the 17th Signal Processing for Space Communications Workshop (ASMS/SPSC) (2022, September)

Conventional multi-beam pattern design in Geostationary (GEO) satellite communication systems consists of a regular grid of non-reconfigurable beams, where the beams overlap is typically assumed at the ... [more ▼]

Conventional multi-beam pattern design in Geostationary (GEO) satellite communication systems consists of a regular grid of non-reconfigurable beams, where the beams overlap is typically assumed at the point where the beam edge reaches a 3-dB loss in the antenna pattern (with respect to the beam center). For certain high demand areas, this 3dB loss has a significant impact. To overcome this issue, in this paper we evaluate the potential gain of beam densification, i.e. considering an increased number of beams (keeping the same beam size and shape) to cover hot-spot areas, with the aim to push the beam overlap and increase the beam gain. In particular, we compare two beam patterns (kindly provided by ESA): One with regular beam grid, and one with densification in a particular hot-spot area. We provide a comparison in terms of per-beam average SINR and capacity, as well as an overall system analysis considering the whole densified region. [less ▲]

Learning to Optimize: Balancing Two Conflict Metrics in MB-HTS Networks

in Advanced Satellite Multimedia Conference / Signal Processing for Space Communications Workshop (ASMS), Gratz, Viena, Sept. 2022 (2022, September)

Matching Traffic Demand in GEO Multibeam Satellites: The Joint Use of Dynamic Beamforming and Precoding Under Practical Constraints

in IEEE Transactions on Broadcasting (2022)

To adjust for the non-uniform spatiotemporal nature of traffic patterns, next-generation high throughput satellite (HTS) systems can benefit from recent technological advancements in the space-segment in ... [more ▼]

To adjust for the non-uniform spatiotemporal nature of traffic patterns, next-generation high throughput satellite (HTS) systems can benefit from recent technological advancements in the space-segment in order to dynamically design traffic-adaptive beam layout plans (ABLPs). In this work, we propose a framework for dynamic beamforming (DBF) optimization and adaptation in dynamic environments. Given realistic traffic patterns and a limited power budget, we propose a feasible DBF operation for a geostationary multibeam HTS network. The goal is to minimize the mismatch between the traffic demand and the offered capacity under practical constraints. These constraints are dictated by the traffic-aware design requirements, the on-board antenna system limitations, and the signaling considerations in the K-band. Noting that the ABLP is agnostic about the inherent inter-beam interference (IBI), we construct an interference simulation environment using irregularly shaped beams for a large-scale multibeam HTS system. To cope with IBI, the combination of on-board DBF and on-ground precoding is considered. For precoded and non-precoded HTS configurations, the proposed design shows better traffic-matching capabilities in comparison to a regular beam layout plan. Lastly, we provide trade-off analyses between system-level key performance indicators for different realistic non-uniform traffic patterns. [less ▲]

Inbound Carrier Plan Optimization for Adaptive VSAT Networks

in IEEE Transactions on Aerospace and Electronic Systems (2022)

The past decades witnessed the application of adaptive modulation and coding (ACM) in satellite links. However, ACM technologies come at the cost of higher complexity when designing the network’s carrier ... [more ▼]

The past decades witnessed the application of adaptive modulation and coding (ACM) in satellite links. However, ACM technologies come at the cost of higher complexity when designing the network’s carrier plan and user terminals. Accounting for those issues is even more important when the satellite link uses frequencies in Ka band and above, where the attenuation caused by tropospheric phenomena is a major concern. In this paper, we propose a solution for the inbound, i.e. return link, carrier plan sizing of very small aperture terminal (VSAT) networks. As tropospheric attenuation is a key factor, we present a mathematical problem formulation based on spatially correlated attenuation time series generators. Our proposed sizing scheme is formulated as a mixed integer linear programming (MILP) optimization problem. The numerical results for a test scenario in Europe show a 10 to 50% bandwidth improvement over traditional sizing methods for outage probabilities lower than 1%. [less ▲]